{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple OOP example with Linear Regression\n",
    "### Dr. Tirthajyoti Sarkar, Fremont, CA 94536\n",
    "\n",
    "In this notebook, we will show how to leverage the power and flexibility of the Object-oriented programming (OOP) paradigm for machine learning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A very simple class `MyLinearRegression`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyLinearRegression:\n",
    "    \n",
    "    def __init__(self, fit_intercept=True):\n",
    "        self.coef_ = None\n",
    "        self.intercept_ = None\n",
    "        self._fit_intercept = fit_intercept"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Create an instance and check attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlr = MyLinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr._fit_intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr.coef_==None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr.intercept_ == None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<__main__.MyLinearRegression object at 0x000001FDD2BA2AC8>\n"
     ]
    }
   ],
   "source": [
    "print(mlr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Built-in description method\n",
    "We can add a special built-in method `__repr__` to create a short description string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyLinearRegression:\n",
    "    \n",
    "    def __init__(self, fit_intercept=True):\n",
    "        self.coef_ = None\n",
    "        self.intercept_ = None\n",
    "        self._fit_intercept = fit_intercept\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return \"I am a Linear Regression model!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlr = MyLinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am a Linear Regression model!\n"
     ]
    }
   ],
   "source": [
    "print(mlr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Adding the `fit` method\n",
    "Now, we can add the core fitting method called `fit`. This uses linear algebra routines from NumPy to solve a linear regression (single or multi-variate) problem."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyLinearRegression:\n",
    "    \n",
    "    def __init__(self, fit_intercept=True):\n",
    "        self.coef_ = None\n",
    "        self.intercept_ = None\n",
    "        self._fit_intercept = fit_intercept\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return \"I am a Linear Regression model!\"\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        Fit model coefficients.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array \n",
    "        y: 1D numpy array\n",
    "        \"\"\"\n",
    "        \n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "            \n",
    "        # add bias if fit_intercept is True\n",
    "        if self._fit_intercept:\n",
    "            X_biased = np.c_[np.ones(X.shape[0]), X]\n",
    "        else:\n",
    "            X_biased = X\n",
    "        \n",
    "        # closed form solution\n",
    "        xTx = np.dot(X_biased.T, X_biased)\n",
    "        inverse_xTx = np.linalg.inv(xTx)\n",
    "        xTy = np.dot(X_biased.T, y)\n",
    "        coef = np.dot(inverse_xTx, xTy)\n",
    "        \n",
    "        # set attributes\n",
    "        if self._fit_intercept:\n",
    "            self.intercept_ = coef[0]\n",
    "            self.coef_ = coef[1:]\n",
    "        else:\n",
    "            self.intercept_ = 0\n",
    "            self.coef_ = coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Generate some random data for test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = 10*np.random.random(size=(20,2))\n",
    "y = 3.5*X.T[0]-1.2*X.T[1]+2*np.random.randn(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0mJxoEcDkRIt9u7d6XKGkh+3aNsm+3VvNExoKnqQnaSAmWuN8bO9U1WFIGmLDfiKhmsM9yJIkSVKJBbIkSZJUYoEsSZIklaxYIEfEJRHxoYi4PSJui4hfLdqfGBE3RsQdxd8L+h+uJDWTuViSBmc1e5AfAt6Umc8ALgN+KSKeCewFbsrMpwE3FY8lqVYOz8xy+f6jXLr3/Vy+/yiHZ2arDmk55mJJPVWj/DdwKxbImXlPZn6quP9t4HZgErgSuK542XXArn4FKUn9cHhmlmsOHWN2bp4EZufmuebQsaHcSJiLJfVSnfJfFSIzV//iiM3AR4BnA1/JzInSc/dn5qN+2ouIq4GrATZu3Pi8gwcPdhXgyZMn2bBhQ1fT1J19boam9XkY+3v8q9/mwdNnHtV+3tg5bHny96x7/sv1eceOHbdm5va1zreKXDwKhnEdHISm9hvs+9n63u/8V5VuP/Pl8vGqC+SI2AB8GPjdzDwUEXOrScpl27dvz1tuuWXVQQNMT08zNTXV1TR1Z5+bodznwzOzHDhynBNz82yaaLFn55aRGwt0GD/jS/e+n6UyYABf3P+ydc9/uT5HxJoL5Kpy8bBbzXdoGNfBQWhqv8G+n63v/c5/Ven2M18uH69qFIuIGAf+AXh3Zh4qmu+NiIuK5y8C7lt1NJIe5s9c1dk00eqqvWrm4qX5HZK6V7f8N2irGcUigLcDt2fmH5aeei9wVXH/KuA9vQ9PGn0Hjhxn/tTpBW3zp05z4MjxiiJqjj07t9AaH1vQ1hofY8/OLRVFtDxz8fL8Dkndq1P+q8JqLjV9OfAa4FhEfLpo+y1gP/B3EfF64CvAK/sTojTaTszNd9Wu3un8BF+Tw1vMxcvwOyR1r2b5b+BWLJAz86O0D0lZyot6G440Gro5pnjTRIvZJTbk/sw1GLu2TdZig2AuXp7fIWlt6pL/quCV9KQe6/Z4yGH7mctxMVU3O57+pK7apQ7znZZjgSz1WLfHQ+7aNsm+3VuZnGgRwOREi327t1byX70nO6mOPvS5r3XVLoH5Tme3mmOQJXVhLcdDDsvPXGcr7ochPmkpHoOstTDf6Wzcgyz1WJ2HzrHQUB3V+Tun6pjvdDYWyFKPDdsxxd2w0FAd1fk7p+qY73Q2FshSjw3TMcXdstBQHdX5O6fqmO90Nh6DLPXBsBxT3C3HxVRd1fU7p+qY73Q2FsiSFrDQkNQU5jstx0MsJEmSpBILZEmSJKnEAlmSJEkqsUCWJEmSSiyQJUmSpBILZEmSJKnEAlmSJEkqcRxkaYAOz8w+PCj93uecYW5m1jE4JUlDq7zdatLFVCyQpQE5PDPLNYeOMX/qNAAPnj7DNYeOATQi2UiS6mXxdmt2br4x2y0PsZAG5MCR4w8nmY75U6c5cOR4RRFJkrS8Jm+3LJClATkxN99VuyRJVWrydssCWRqQTROtrtolSapSk7dbFsjSgOzZuYXW+NiCttb4GHt2bqkoIkmSltfk7ZYn6Y24pp59Oow6y73zeZw3dg77dm/181BjmI+kelm83WrS99YCeYQ1+ezTYbVr2+TDy356epopPwc1hPlIqqfydqtJPMRihDX57FNJw8V8JKlOLJBHWJPPPpU0XMxHkurEAnmENfnsU0nDxXwkqU4skEdYk88+lTRczEeS6mTFAjki3hER90XEZ0ptT4yIGyPijuLvBf0NU2uxa9sk+3ZvZXKiRQCTEy1HTZBqrM752HwkqU5WM4rFu4A/Bf57qW0vcFNm7o+IvcXjf9/78LReTT37VBpR76LG+dh8JKkuVtyDnJkfAb6xqPlK4Lri/nXArh7HJUlaxHwsSYMRmbnyiyI2A+/LzGcXj+cyc6L0/P2ZueTPehFxNXA1wMaNG5938ODBrgI8efIkGzZs6GqaurPPzdC0Pjetv7B8n3fs2HFrZm5fyzzXmo/Xm4tHQRPXQWhuv8G+N7Hv3fZ7uXzc9wuFZOa1wLUA27dvz6mpqa6mn56epttp6s4+N0PT+ty0/sJw9Xm9uXgUDNPnMUhN7TfY9yb2vVf9XusoFvdGxEUAxd/71h2JJGktzMeS1GNrLZDfC1xV3L8KeE9vwpEkdcl8LEk9tpph3q4HPg5siYi7I+L1wH7gJRFxB/CS4rEkqY/Mx5I0GCseg5yZr17mqRf1OBZJ0lmYjyVpMLySniRJklRigSxJkiSVWCBLkiRJJRbIkiRJUokFsiRJklRigSxJkiSV9P1S0xpth2dmOXDkOCfm5tk00WLPzi3s2jZZdViSpCHitkJ1Y4GsNTs8M8s1h44xf+o0ALNz81xz6BiAiU+SBLitUD15iIXW7MCR4w8nvI75U6c5cOR4RRFJkoaN2wrVkQWy1uzE3HxX7ZKk5nFboTqyQNaabZpoddUuSWoetxWqIwtkrdmenVtojY8taGuNj7Fn55aKIpIkDRu3FaojT9LTmnVOrvDMZEnSctxWqI4skLUuu7ZNmuQkSWfltkJ14yEWkiRJUol7kNUIDlIvSdJoWWrbPtGjebsHWSOvM0j97Nw8ySOD1B+ema06NEmStAbLbdvn5k/1ZP7uQdaq1HkP7NkGqa9LHyRJ6sbc/Cku33+0ltvt1Vhu237vNy2QNSB1v0yog9RLkprk8Mwss/fPMzvXHl6vbtvt1VhuG/7g6TM9mb+HWGhFdb9MqIPUS5Ka5MCR45zJXNBWp+32aiy3DT9vrDelrQWyVlT3PbAOUi9JapK6b7dXY7lt+8YnPLYn87dA1orqvgd217ZJ9u3eyuREiwAmJ1rs2711ZH5mkiSprO7b7dVYbts+0Rrvyfw9Blkr2rNzy4JjkKF+e2AdpF6S1BR7dm5h9vZbF7TVbbu9Gktt26en7+jJvN2DrBW5B1aSpPrYtW2SyQtabrfXwT3IA1TnodLcAytJ/VPn7YOG00RrnI/tnao6jNqyQB6Qug+VJknqD7cP0vBZ1yEWEXFFRByPiDsjYm+vghoWh2dmuXz/US7d+34u3390XVdeq/tQaZKG26jn41Hm9mG09LJ2UHXWvAc5IsaAtwIvAe4GPhkR783Mz/YquCr1+j/6Jgy5Iqkao56PR53bh9HhrwGjYz17kJ8P3JmZX8jMB4GDwJW9Cat6vf6PvglDrkiqzEjn41Hn9mF0+GvA6IhcdKWVVU8Y8VPAFZn5huLxa4AXZOYbF73uauBqgI0bNz7v4MGDXb3PyZMn2bBhw5piXI9js99c9rmtk0/oen5z86eYvX9+wZVtzolg8oLWo8bsq6rPVbLPo69p/YXl+7xjx45bM3N7r95nNfl4vbl4FAzrOtjN9mEthrXfgzDovve6dliPpn7u3fZ7uXy8npP0Yom2R1XbmXktcC3A9u3bc2pqqqs3mZ6epttpeuE/7D/K7BI/b01OtPjln1lbPKs9S7mqPlfJPo++pvUXBtrnFfPxenPxKBjmdbCfo1gMc7/7bdB970ftsFZN/dx71e/1FMh3A5eUHl8MnFhfOMOjHxfHcKg0SX0y0vm4Cdw+jIZRuLCW2tZTIH8SeFpEXArMAq8CfronUQ2BTqJyXEpJNTDS+ViqC2uH0bHmAjkzH4qINwJHgDHgHZl5W88iGwL+Ry+pDpqQj6W6sHYYDeu6UEhmfgD4QI9ikSStkflYknrHK+mNGC9XKkmSqjBKNYgF8ghxgHJJklSFUatB1nWpaQ0XByiXJElVGLUaxAJ5hHi5UkmSVIVRq0EskEeIlyuVJElVGLUaxAJ5hOzZuYXW+NiCNgcolyRJ/TZqNYgn6Y0QByiXJElVGLUaZOgL5Ln5U1y+/+hILOxBcIBySVKvjNKwXeq/UapBhrpAPjwzy+z988zOtXfZ133IEEmS6mLUhu2SujHUxyAfOHKcM5kL2uo8ZIgkSXUxasN2Sd0Y6gJ51IYMkSSpLtwGq8mGukAetSFDJEmqC7fBarKhLpD37NzCOREL2uo8ZIgkSXUxasN2Sd0Y6gJ517ZJJi9oMTnRIoDJiRb7dm/15ABJkvps17ZJ9u3e6jZYjTTUo1gATLTG+djeqarDkCSpcUZp2C6pG0O9B1mSJEkaNAtkSZIkqcQCWZIkSSqxQJYkSZJKhv4kvWHl9eklSZJ6bxhqLAvkNfD69JIkSb03LDWWh1isgdenlyRJ6r1hqbEskNfA69NLkiT13rDUWBbIa+D16SVJknpvWGosC+Q18Pr0kiRJvTcsNZYn6a1B5yDxqs+wlCRJGiXDUmNZIK+R16eXJEnqvWGosTzEQpIkSSqxQJYkSZJKLJAlSZKkksjMwb1ZxNeAL3c52YXA1/sQzjCzz83QtD43rb+wfJ+fkplPGnQwHWvMxaOgiesgNLffYN+b2Pdu+71kPh5ogbwWEXFLZm6vOo5Bss/N0LQ+N62/0Mw+D7Omfh5N7TfY9yb2vVf99hALSZIkqcQCWZIkSSqpQ4F8bdUBVMA+N0PT+ty0/kIz+zzMmvp5NLXfYN+bqCf9HvpjkCVJkqRBqsMeZEmSJGlgLJAlSZKkkqEtkCPiiog4HhF3RsTequPpt4i4JCI+FBG3R8RtEfGrVcc0KBExFhEzEfG+qmMZhIiYiIgbIuJzxef9o1XH1G8R8evFev2ZiLg+Ih5bdUy9FhHviIj7IuIzpbYnRsSNEXFH8feCKmNsoibn1o6m5diOJuZaaEa+7ehn3h3KAjkixoC3Ai8Fngm8OiKeWW1UffcQ8KbMfAZwGfBLDehzx68Ct1cdxAD9CfC/MvPpwA8z4n2PiEngV4DtmflsYAx4VbVR9cW7gCsWte0FbsrMpwE3FY81WE3OrR1Ny7Edjcq10Kh82/Eu+pR3h7JABp4P3JmZX8jMB4GDwJUVx9RXmXlPZn6quP9t2l/kyWqj6r+IuBh4GfC2qmMZhIh4PPAvgLcDZOaDmTlXbVQDcS7QiohzgccBJyqOp+cy8yPANxY1XwlcV9y/Dtg10KDU2Nza0bQc29HgXAsNyLcd/cy7w1ogTwJ3lR7fTbMS2mZgG3BztZEMxB8DvwmcqTqQAfkB4GvAO4ufPN8WEedXHVQ/ZeYs8BbgK8A9wDcz84PVRjUwGzPzHmgXasD3VRxPozUst3Y0Lcd2NC7XQuPzbUdP8u6wFsixRFsjxqOLiA3APwC/lpnfqjqefoqIlwP3ZeatVccyQOcCzwX+LDO3AQ8w4j+7F8d/XQlcCmwCzo+In602KjVNk3JrR0NzbEfjci2Yb3tpWAvku4FLSo8vZoR/IuiIiHHaCfzdmXmo6ngG4HLgFRHxJdqH0bwwIv662pD67m7g7szs7MG6gXYSH2UvBr6YmV/LzFPAIeDHKo5pUO6NiIsAir/3VRxPIzUwt3Y0Mcd2NDHXQrPzbUdP8u6wFsifBJ4WEZdGxHm0DzB/b8Ux9VVEBO1jpW7PzD+sOp5ByMxrMvPizNxM+zM+mpkj/Z9uZn4VuCsithRNLwI+W2FIg/AV4LKIeFyxnr+IBpwsU3gvcFVx/yrgPRXG0khNzK0dTcyxHQ3NtdDsfNvRk7x7bs/C6aHMfCgi3ggcoX0G5jsy87aKw+q3y4HXAMci4tNF229l5gcqjEn98cvAu4t//r4A/FzF8fRVZt4cETcAn6I9osAMI3gJ1Ii4HpgCLoyIu4H/DOwH/i4iXk97w/XK6iJsLHNrczUq10Jz8m1HP/Oul5qWJEmSSob1EAtJkiSpEhbIkiRJUokFsiRJklRigSxJkiSVWCBLkiRJJRbIkiRJUokFsiRJklTy/wHK718Bg3iCWQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(10,3))\n",
    "\n",
    "ax[0].scatter(X.T[0],y)\n",
    "ax[0].set_title(\"Output vs. first feature\")\n",
    "ax[0].grid(True)\n",
    "ax[1].scatter(X.T[1],y)\n",
    "ax[1].set_title(\"Output vs. second feature\")\n",
    "ax[1].grid(True)\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Instantiate a new `MyLinearRegression` object and fit the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlr = MyLinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have not fitted the data yet. There is no regression coefficients\n",
      "Regression coefficients: None\n"
     ]
    }
   ],
   "source": [
    "print(\"We have not fitted the data yet. There is no regression coefficients\")\n",
    "print(\"Regression coefficients:\", mlr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "We have fitted the data. We can print the regression coefficients now\n",
      "Regression coefficients: [ 3.34778254 -1.38767517]\n"
     ]
    }
   ],
   "source": [
    "print(\"We have fitted the data. We can print the regression coefficients now\")\n",
    "print(\"Regression coefficients:\", mlr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The intercept term is given by:  2.1898165857075256\n"
     ]
    }
   ],
   "source": [
    "print(\"The intercept term is given by: \", mlr.intercept_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Comparison of ground truth and fitted values\n",
    "Woudn't it be nice to compare the ground truth with the predictions and see how closely they fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "coef_ = mlr.coef_\n",
    "y_pred = np.dot(X,coef_)+mlr.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y,y_pred,s=100,alpha=0.75,color='red',edgecolor='k')\n",
    "plt.plot(y,y,c='k',linestyle='dotted')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Encapsulation\n",
    "But we don't want to write stand-alone code. Can we _encapsulate_ the code inside the class?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyLinearRegression:\n",
    "    \n",
    "    def __init__(self, fit_intercept=True):\n",
    "        self.coef_ = None\n",
    "        self.intercept_ = None\n",
    "        self._fit_intercept = fit_intercept\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return \"I am a Linear Regression model!\"\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        Fit model coefficients.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array \n",
    "        y: 1D numpy array\n",
    "        \"\"\"\n",
    "        \n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "            \n",
    "        # add bias if fit_intercept is True\n",
    "        if self._fit_intercept:\n",
    "            X_biased = np.c_[np.ones(X.shape[0]), X]\n",
    "        else:\n",
    "            X_biased = X\n",
    "        \n",
    "        # closed form solution\n",
    "        xTx = np.dot(X_biased.T, X_biased)\n",
    "        inverse_xTx = np.linalg.inv(xTx)\n",
    "        xTy = np.dot(X_biased.T, y)\n",
    "        coef = np.dot(inverse_xTx, xTy)\n",
    "        \n",
    "        # set attributes\n",
    "        if self._fit_intercept:\n",
    "            self.intercept_ = coef[0]\n",
    "            self.coef_ = coef[1:]\n",
    "        else:\n",
    "            self.intercept_ = 0\n",
    "            self.coef_ = coef\n",
    "            \n",
    "        # Predicted/fitted y\n",
    "        self.fitted_ = np.dot(X,mlr.coef_) + mlr.intercept_\n",
    "    \n",
    "    def plot_fitted(self,reference_line=False):\n",
    "        \"\"\"\n",
    "        Plots fitted values against the true output values from the data\n",
    "        \n",
    "        Arguments:\n",
    "        reference_line: A Boolean switch to draw a 45-degree reference line on the plot\n",
    "        \"\"\"\n",
    "        plt.title(\"True vs. fitted values\",fontsize=14)\n",
    "        plt.scatter(y,self.fitted_,s=100,alpha=0.75,color='red',edgecolor='k')\n",
    "        if reference_line:\n",
    "            plt.plot(y,y,c='k',linestyle='dotted')\n",
    "        plt.xlabel(\"True values\")\n",
    "        plt.ylabel(\"Fitted values\")\n",
    "        plt.grid(True)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Demo the new `plot_fitted` method \n",
    "Now the `MyLinearRegression` class has the ability (aka methods) to both fit the data and visualize the fitted/true output values in a plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A fresh instance\n",
    "mlr = MyLinearRegression()\n",
    "# Fitting with the data\n",
    "mlr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Call the 'plot_fitted' method\n",
    "mlr.plot_fitted()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can pass on a switch 'reference_line' to draw a 45-degree reference line on the plot\n",
    "mlr.plot_fitted(reference_line=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Adding a `predict` method\n",
    "Now, we want to extend the functionality and add a `predict` method to enable the class to predict for any arbitrary new dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyLinearRegression:\n",
    "    \n",
    "    def __init__(self, fit_intercept=True):\n",
    "        self.coef_ = None\n",
    "        self.intercept_ = None\n",
    "        self._fit_intercept = fit_intercept\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return \"I am a Linear Regression model!\"\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        Fit model coefficients.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array \n",
    "        y: 1D numpy array\n",
    "        \"\"\"\n",
    "        \n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "            \n",
    "        # add bias if fit_intercept is True\n",
    "        if self._fit_intercept:\n",
    "            X_biased = np.c_[np.ones(X.shape[0]), X]\n",
    "        else:\n",
    "            X_biased = X\n",
    "        \n",
    "        # closed form solution\n",
    "        xTx = np.dot(X_biased.T, X_biased)\n",
    "        inverse_xTx = np.linalg.inv(xTx)\n",
    "        xTy = np.dot(X_biased.T, y)\n",
    "        coef = np.dot(inverse_xTx, xTy)\n",
    "        \n",
    "        # set attributes\n",
    "        if self._fit_intercept:\n",
    "            self.intercept_ = coef[0]\n",
    "            self.coef_ = coef[1:]\n",
    "        else:\n",
    "            self.intercept_ = 0\n",
    "            self.coef_ = coef\n",
    "            \n",
    "        # Predicted/fitted y\n",
    "        self.fitted_ = np.dot(X,mlr.coef_) + mlr.intercept_\n",
    "    \n",
    "    def plot_fitted(self,reference_line=False):\n",
    "        \"\"\"\n",
    "        Plots fitted values against the true output values from the data\n",
    "        \n",
    "        Arguments:\n",
    "        reference_line: A Boolean switch to draw a 45-degree reference line on the plot\n",
    "        \"\"\"\n",
    "        plt.title(\"True vs. fitted values\",fontsize=14)\n",
    "        plt.scatter(y,self.fitted_,s=100,alpha=0.75,color='red',edgecolor='k')\n",
    "        if reference_line:\n",
    "            plt.plot(y,y,c='k',linestyle='dotted')\n",
    "        plt.xlabel(\"True values\")\n",
    "        plt.ylabel(\"Fitted values\")\n",
    "        plt.grid(True)\n",
    "        plt.show()\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"Output model prediction.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array\n",
    "        \"\"\"\n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "        self.predicted_ = self.intercept_ + np.dot(X, self.coef_)\n",
    "        return self.predicted_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Testing `predict` method with new data \n",
    "Note the number of samples is different from the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_new_samples = 10\n",
    "X_new = 10*np.random.random(size=(num_new_samples,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_new = 3.5*X_new.T[0]-1.2*X_new.T[1]+2*np.random.randn(num_new_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlr = MyLinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=mlr.predict(X_new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(y_new,y_pred,s=100,alpha=0.75,color='red',edgecolor='k')\n",
    "plt.plot(y_new,y_new,c='k',linestyle='dotted')\n",
    "plt.xlabel(\"True values\")\n",
    "plt.ylabel(\"Predicted values\")\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Moving towards regression metrics - degrees of freedom\n",
    "We will now move towards regression metrics (and statistical inference). For that, we first need to introduce few more attributes associated with the dataset - degrees of freedom. They will be computed when we try to fit a dataset. They will be used later to compute metric like $\\textbf{adjusted } R^2$.\n",
    "\n",
    "`dft_` : degrees of freedom of the estimate of the population variance of the dependent variable<br>\n",
    "`dfe_` : degrees of freedom of the estimate of the underlying population error variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyLinearRegression:\n",
    "    \n",
    "    def __init__(self, fit_intercept=True):\n",
    "        self.coef_ = None\n",
    "        self.intercept_ = None\n",
    "        self._fit_intercept = fit_intercept\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return \"I am a Linear Regression model!\"\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        Fit model coefficients.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array \n",
    "        y: 1D numpy array\n",
    "        \"\"\"\n",
    "        \n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "        \n",
    "        # features and data\n",
    "        self.features_ = X\n",
    "        self.target__ = y\n",
    "        \n",
    "        # degrees of freedom of population dependent variable variance\n",
    "        self.dft_ = X.shape[0] - 1   \n",
    "        # degrees of freedom of population error variance\n",
    "        self.dfe_ = X.shape[0] - X.shape[1] - 1\n",
    "            \n",
    "        # add bias if fit_intercept is True\n",
    "        if self._fit_intercept:\n",
    "            X_biased = np.c_[np.ones(X.shape[0]), X]\n",
    "        else:\n",
    "            X_biased = X\n",
    "        \n",
    "        # closed form solution\n",
    "        xTx = np.dot(X_biased.T, X_biased)\n",
    "        inverse_xTx = np.linalg.inv(xTx)\n",
    "        xTy = np.dot(X_biased.T, y)\n",
    "        coef = np.dot(inverse_xTx, xTy)\n",
    "        \n",
    "        # set attributes\n",
    "        if self._fit_intercept:\n",
    "            self.intercept_ = coef[0]\n",
    "            self.coef_ = coef[1:]\n",
    "        else:\n",
    "            self.intercept_ = 0\n",
    "            self.coef_ = coef\n",
    "            \n",
    "        # Predicted/fitted y\n",
    "        self.fitted_ = np.dot(X,mlr.coef_) + mlr.intercept_\n",
    "    \n",
    "    def plot_fitted(self,reference_line=False):\n",
    "        \"\"\"\n",
    "        Plots fitted values against the true output values from the data\n",
    "        \n",
    "        Arguments:\n",
    "        reference_line: A Boolean switch to draw a 45-degree reference line on the plot\n",
    "        \"\"\"\n",
    "        plt.scatter(y,self.fitted_,s=100,alpha=0.75,color='red',edgecolor='k')\n",
    "        if reference_line:\n",
    "            plt.plot(y,y,c='k',linestyle='dotted')\n",
    "        plt.xlabel(\"True values\")\n",
    "        plt.ylabel(\"Fitted values\")\n",
    "        plt.grid(True)\n",
    "        plt.show()\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"Output model prediction.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array\n",
    "        \"\"\"\n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "        self.predicted_ = self.intercept_ + np.dot(X, self.coef_)\n",
    "        return self.predicted_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlr = MyLinearRegression()\n",
    "mlr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr.dfe_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr.dft_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### `Metrics` class\n",
    "We could have added a whole bunch of methods directly into the `MyLinearRegression` class. But, instead, we will show the power of inheritance and define a separate class `Metrics` for computing common metrics of a regression model.\n",
    "\n",
    "Note, this class has no `__init__` method because we will never instantiate an object using this class. Rather, we will sort of absorb this class into the `MyLinearRegression` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Metrics:\n",
    "              \n",
    "    def sse(self):\n",
    "        '''returns sum of squared errors (model vs actual)'''\n",
    "        squared_errors = (self.resid_) ** 2\n",
    "        self.sq_error_ = np.sum(squared_errors)\n",
    "        return self.sq_error_\n",
    "        \n",
    "    def sst(self):\n",
    "        '''returns total sum of squared errors (actual vs avg(actual))'''\n",
    "        avg_y = np.mean(self.target_)\n",
    "        squared_errors = (self.target_ - avg_y) ** 2\n",
    "        self.sst_ = np.sum(squared_errors)\n",
    "        return self.sst_\n",
    "    \n",
    "    def r_squared(self):\n",
    "        '''returns calculated value of r^2'''\n",
    "        self.r_sq_ = 1 - self.sse()/self.sst()\n",
    "        return self.r_sq_\n",
    "    \n",
    "    def adj_r_squared(self):\n",
    "        '''returns calculated value of adjusted r^2'''\n",
    "        self.adj_r_sq_ = 1 - (self.sse()/self.dfe_) / (self.sst()/self.dft_)\n",
    "        return self.adj_r_sq_\n",
    "    \n",
    "    def mse(self):\n",
    "        '''returns calculated value of mse'''\n",
    "        self.mse_ = np.mean( (self.predict(self.features_) - self.target_) ** 2 )\n",
    "        return self.mse_\n",
    "    \n",
    "    def pretty_print_stats(self):\n",
    "        '''returns report of statistics for a given model object'''\n",
    "        items = ( ('sse:', self.sse()), ('sst:', self.sst()), \n",
    "                 ('mse:', self.mse()), ('r^2:', self.r_squared()), \n",
    "                  ('adj_r^2:', self.adj_r_squared()))\n",
    "        for item in items:\n",
    "            print('{0:8} {1:.4f}'.format(item[0], item[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Class with inheritance\n",
    "Now we inherit the `Metrics` class in the `MyLinearRegression` class by passing on `Metrics` in the very defination of the `MyLinearRegression` class.\n",
    "\n",
    "We also need to add a new attribute - `resid_`. These are the residuals (the difference between the fitted values and true target_/output values), which are used by the methods in the `Metrics` perform the necessary computations. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyLinearRegression(Metrics):\n",
    "    \n",
    "    def __init__(self, fit_intercept=True):\n",
    "        self.coef_ = None\n",
    "        self.intercept_ = None\n",
    "        self._fit_intercept = fit_intercept\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return \"I am a Linear Regression model!\"\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        Fit model coefficients.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array \n",
    "        y: 1D numpy array\n",
    "        \"\"\"\n",
    "        \n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "        \n",
    "        # features and data\n",
    "        self.features_ = X\n",
    "        self.target_ = y\n",
    "        \n",
    "        # degrees of freedom of population dependent variable variance\n",
    "        self.dft_ = X.shape[0] - 1   \n",
    "        # degrees of freedom of population error variance\n",
    "        self.dfe_ = X.shape[0] - X.shape[1] - 1\n",
    "            \n",
    "        # add bias if fit_intercept is True\n",
    "        if self._fit_intercept:\n",
    "            X_biased = np.c_[np.ones(X.shape[0]), X]\n",
    "        else:\n",
    "            X_biased = X\n",
    "        \n",
    "        # closed form solution\n",
    "        xTx = np.dot(X_biased.T, X_biased)\n",
    "        inverse_xTx = np.linalg.inv(xTx)\n",
    "        xTy = np.dot(X_biased.T, y)\n",
    "        coef = np.dot(inverse_xTx, xTy)\n",
    "        \n",
    "        # set attributes\n",
    "        if self._fit_intercept:\n",
    "            self.intercept_ = coef[0]\n",
    "            self.coef_ = coef[1:]\n",
    "        else:\n",
    "            self.intercept_ = 0\n",
    "            self.coef_ = coef\n",
    "            \n",
    "        # Predicted/fitted y\n",
    "        self.fitted_ = np.dot(X,mlr.coef_) + mlr.intercept_\n",
    "        \n",
    "        # Residuals\n",
    "        residuals = self.target_ - self.fitted_\n",
    "        self.resid_ = residuals\n",
    "    \n",
    "    def plot_fitted(self,reference_line=False):\n",
    "        \"\"\"\n",
    "        Plots fitted values against the true output values from the data\n",
    "        \n",
    "        Arguments:\n",
    "        reference_line: A Boolean switch to draw a 45-degree reference line on the plot\n",
    "        \"\"\"\n",
    "        plt.title(\"True vs. fitted values\",fontsize=14)\n",
    "        plt.scatter(y,self.fitted_,s=100,alpha=0.75,color='red',edgecolor='k')\n",
    "        if reference_line:\n",
    "            plt.plot(y,y,c='k',linestyle='dotted')\n",
    "        plt.xlabel(\"True values\")\n",
    "        plt.ylabel(\"Fitted values\")\n",
    "        plt.grid(True)\n",
    "        plt.show()\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"Output model prediction.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array\n",
    "        \"\"\"\n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "        self.predicted_ = self.intercept_ + np.dot(X, self.coef_)\n",
    "        return self.predicted_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Demo the newly acquired power of `MyLinearRegression` - the metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlr = MyLinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "fit=mlr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52.29620169650658"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr.sse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2952.6956292134073"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr.sst()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9822886581403454"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr.r_squared()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sse:     52.2962\n",
      "sst:     2952.6956\n",
      "mse:     2.6148\n",
      "r^2:     0.9823\n",
      "adj_r^2: 0.9802\n"
     ]
    }
   ],
   "source": [
    "mlr.pretty_print_stats()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.11202629,  0.18163423,  0.87380481, -1.31987254, -1.27503193,\n",
       "        0.28947718, -1.89527517, -0.56991112, -0.87346478, -0.28187532,\n",
       "        2.99303852,  1.82098677,  0.65175513,  0.87339867, -1.6227975 ,\n",
       "        1.62232471, -0.55441197,  3.85246154, -2.69298212, -0.96123283])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlr.resid_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Visual diagnostics\n",
    "The success of a linear regression model depends on some fundamental assumptions about the nature of the underlying data that it tries to model. [See this article](https://www.jmp.com/en_us/statistics-knowledge-portal/what-is-regression/simple-linear-regression-assumptions.html) for a simple and intuitive understanding of these assumptions.\n",
    "\n",
    "It is, therefore, extremely important to check the quality of your linear regression model, by verifying whether these assumptions were “reasonably” satisfied (generally visual analytics methods, which are subject to interpretation, are used to check the assumptions).\n",
    "\n",
    "Visual diagnostics play a crucial part in this quality check. Following plots can be constructed from the any linear regression fitted model. They can be termed diagnostics.\n",
    "\n",
    "* Residuals vs. predicting variables plots\n",
    "* Fitted vs. residuals plot\n",
    "* Histogram of the normalized residuals\n",
    "* Q-Q plot of the normalized residuals\n",
    "\n",
    "[See this article](https://towardsdatascience.com/how-do-you-check-the-quality-of-your-regression-model-in-python-fa61759ff685) for a more detailed discussion and the general approach. Here, we will add these visual diagnostics to the `MyLinearRegression` class.\n",
    "\n",
    "As an instance, let's plot the fitted vs. residuals plot. Ideally, this plot should show no pattern, residuals distributed completely randomly around the zero line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(mlr.fitted_,mlr.resid_)\n",
    "plt.hlines(y=0,xmin=np.amin(mlr.fitted_),xmax=np.amax(mlr.fitted_),color='k',linestyle='dashed')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Creating a separate `Diagnostics_plots` class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Diagnostics_plots:\n",
    "    \n",
    "    def __init__():\n",
    "        pass\n",
    "    \n",
    "    def fitted_vs_residual(self):\n",
    "        '''Plots fitted values vs. residuals'''\n",
    "        plt.title(\"Fitted vs. residuals plot\",fontsize=14)\n",
    "        plt.scatter(self.fitted_,self.resid_,edgecolor='k')\n",
    "        plt.hlines(y=0,xmin=np.amin(self.fitted_),xmax=np.amax(self.fitted_),color='k',linestyle='dashed')\n",
    "        plt.xlabel(\"Fitted values\")\n",
    "        plt.ylabel(\"Residuals\")\n",
    "        plt.show()\n",
    "    \n",
    "    def fitted_vs_features(self):\n",
    "        '''Plots residuals vs all feature variables in a grid'''\n",
    "        num_plots = self.features_.shape[1]\n",
    "        if num_plots%3==0:\n",
    "            nrows = int(num_plots/3)\n",
    "        else:\n",
    "            nrows = int(num_plots/3)+1\n",
    "        ncols = 3\n",
    "        fig, ax = plt.subplots(nrows, ncols, figsize=(15,nrows*3.5))\n",
    "        axes = ax.ravel()\n",
    "        for i in range(num_plots,nrows*ncols):\n",
    "            axes[i].set_visible(False)\n",
    "        for i in range(num_plots):\n",
    "            axes[i].scatter(self.features_.T[i],self.resid_,color='orange',edgecolor='k',alpha=0.8)\n",
    "            axes[i].grid(True)\n",
    "            axes[i].set_xlabel(\"Feature X[{}]\".format(i))\n",
    "            axes[i].set_ylabel(\"Residuals\")\n",
    "            axes[i].hlines(y=0,xmin=np.amin(self.features_.T[i]),xmax=np.amax(self.features_.T[i]),\n",
    "                           color='k',linestyle='dashed')\n",
    "        plt.show()\n",
    "        \n",
    "    def histogram_resid(self,normalized=True):\n",
    "        '''Plots a histogram of the residuals (can be normalized)'''\n",
    "        if normalized:\n",
    "            norm_r=self.resid_/np.linalg.norm(self.resid_)\n",
    "        else:\n",
    "            norm_r = self.resid_\n",
    "        num_bins=min(20,int(np.sqrt(self.features_.shape[0])))\n",
    "        plt.title(\"Histogram of the normalized residuals\")\n",
    "        plt.hist(norm_r,bins=num_bins,edgecolor='k')\n",
    "        plt.xlabel(\"Normalized residuals\")\n",
    "        plt.ylabel(\"Count\")\n",
    "        plt.show()\n",
    "    \n",
    "    def shapiro_test(self,normalized=True):\n",
    "        '''Performs Shapiro-Wilk normality test on the residuals'''\n",
    "        from scipy.stats import shapiro\n",
    "        if normalized:\n",
    "            norm_r=self.resid_/np.linalg.norm(self.resid_)\n",
    "        else:\n",
    "            norm_r = self.resid_\n",
    "        _,p = shapiro(norm_r)\n",
    "        if p > 0.01:\n",
    "            print(\"The residuals seem to have come from a Gaussian process\")\n",
    "        else:\n",
    "            print(\"The residuals does not seem to have come from a Gaussian process. \\\n",
    "            \\nNormality assumptions of the linear regression may have been violated.\")\n",
    "        \n",
    "    def qqplot_resid(self,normalized=True):\n",
    "        '''Creates a quantile-quantile plot for residuals comparing with a normal distribution'''\n",
    "        from scipy.stats import probplot\n",
    "        if normalized:\n",
    "            norm_r=self.resid_/np.linalg.norm(self.resid_)\n",
    "        else:\n",
    "            norm_r = self.resid_\n",
    "        plt.title(\"Q-Q plot of the normalized residuals\")\n",
    "        probplot(norm_r,dist='norm',plot=plt)\n",
    "        plt.xlabel(\"Theoretical quantiles\")\n",
    "        plt.ylabel(\"Residual quantiles\")\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Inheritance from more than one classes\n",
    "We can inherit from more than one classes. Already, we have defined `MyLinearRegression` so as to inherit from `Metrics` class. We can add `Diagnostic_plots` to the list too."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyLinearRegression(Metrics, Diagnostics_plots):\n",
    "    \n",
    "    def __init__(self, fit_intercept=True):\n",
    "        self.coef_ = None\n",
    "        self.intercept_ = None\n",
    "        self._fit_intercept = fit_intercept\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return \"I am a Linear Regression model!\"\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        Fit model coefficients.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array \n",
    "        y: 1D numpy array\n",
    "        \"\"\"\n",
    "        \n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "        \n",
    "        # features and data\n",
    "        self.features_ = X\n",
    "        self.target_ = y\n",
    "        \n",
    "        # degrees of freedom of population dependent variable variance\n",
    "        self.dft_ = X.shape[0] - 1   \n",
    "        # degrees of freedom of population error variance\n",
    "        self.dfe_ = X.shape[0] - X.shape[1] - 1\n",
    "            \n",
    "        # add bias if fit_intercept is True\n",
    "        if self._fit_intercept:\n",
    "            X_biased = np.c_[np.ones(X.shape[0]), X]\n",
    "        else:\n",
    "            X_biased = X\n",
    "        \n",
    "        # closed form solution\n",
    "        xTx = np.dot(X_biased.T, X_biased)\n",
    "        inverse_xTx = np.linalg.inv(xTx)\n",
    "        xTy = np.dot(X_biased.T, y)\n",
    "        coef = np.dot(inverse_xTx, xTy)\n",
    "        \n",
    "        # set attributes\n",
    "        if self._fit_intercept:\n",
    "            self.intercept_ = coef[0]\n",
    "            self.coef_ = coef[1:]\n",
    "        else:\n",
    "            self.intercept_ = 0\n",
    "            self.coef_ = coef\n",
    "            \n",
    "        # Predicted/fitted y\n",
    "        self.fitted_ = np.dot(X,mlr.coef_) + mlr.intercept_\n",
    "        \n",
    "        # Residuals\n",
    "        residuals = self.target_ - self.fitted_\n",
    "        self.resid_ = residuals\n",
    "    \n",
    "    def plot_fitted(self,reference_line=False):\n",
    "        \"\"\"\n",
    "        Plots fitted values against the true output values from the data\n",
    "        \n",
    "        Arguments:\n",
    "        reference_line: A Boolean switch to draw a 45-degree reference line on the plot\n",
    "        \"\"\"\n",
    "        plt.title(\"True vs. fitted values\",fontsize=14)\n",
    "        plt.scatter(y,self.fitted_,s=100,alpha=0.75,color='red',edgecolor='k')\n",
    "        if reference_line:\n",
    "            plt.plot(y,y,c='k',linestyle='dotted')\n",
    "        plt.xlabel(\"True values\")\n",
    "        plt.ylabel(\"Fitted values\")\n",
    "        plt.grid(True)\n",
    "        plt.show()\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"Output model prediction.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array\n",
    "        \"\"\"\n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "        self.predicted_ = self.intercept_ + np.dot(X, self.coef_)\n",
    "        return self.predicted_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Testing diagnostics plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_samples=100\n",
    "num_dim = 2\n",
    "X = 10*np.random.random(size=(num_samples,num_dim))\n",
    "y = 3.5*X.T[0]-1.2*X.T[1]+2*np.random.randn(num_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlr = MyLinearRegression()\n",
    "mlr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlr.fitted_vs_residual()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlr.histogram_resid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlr.qqplot_resid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_samples=100\n",
    "num_dim = 5\n",
    "X = 10*np.random.random(size=(num_samples,num_dim))\n",
    "coeff = np.array([2,-3.5,1.2,4.1,-2.5])\n",
    "y = np.dot(coeff,X.T)+2*np.random.randn(num_samples)\n",
    "\n",
    "mlr.fit(X,y)\n",
    "mlr.fitted_vs_features()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The residuals seem to have come from a Gaussian process\n"
     ]
    }
   ],
   "source": [
    "mlr.shapiro_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlr.histogram_resid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlr.fitted_vs_residual()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlr.plot_fitted()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Moving normal plot methods to a separate class\n",
    "We saw the power of inheritance. Therefore, to de-clutter the main class definition, we should remove the plot methods to a separate plotting class. This is also a time-tested principle of OOP that methods, which can be grouped under a common category, should have their own class, which can be inherited by one main class.\n",
    "\n",
    "We define a `Data_plots` class which now contains the `plot_fitted` method. We also add a general pairwise plot functionality to this class using the `pairplot` from `Seaborn` library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Data_plots:\n",
    "    \n",
    "    def __init__():\n",
    "        pass\n",
    "    \n",
    "    def pairplot(self):\n",
    "        '''Creates pairplot of all variables and the target using the Seaborn library'''\n",
    "        \n",
    "        print (\"This may take a little time. Have patience...\")\n",
    "        from seaborn import pairplot\n",
    "        from pandas import DataFrame\n",
    "        df = DataFrame(np.hstack((self.features_,self.target_.reshape(-1,1))))\n",
    "        pairplot(df)\n",
    "        plt.show()\n",
    "    \n",
    "    def plot_fitted(self,reference_line=False):\n",
    "        \"\"\"\n",
    "        Plots fitted values against the true output values from the data\n",
    "        \n",
    "        Arguments:\n",
    "        reference_line: A Boolean switch to draw a 45-degree reference line on the plot\n",
    "        \"\"\"\n",
    "        plt.title(\"True vs. fitted values\",fontsize=14)\n",
    "        plt.scatter(y,self.fitted_,s=100,alpha=0.75,color='red',edgecolor='k')\n",
    "        if reference_line:\n",
    "            plt.plot(y,y,c='k',linestyle='dotted')\n",
    "        plt.xlabel(\"True values\")\n",
    "        plt.ylabel(\"Fitted values\")\n",
    "        plt.grid(True)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyLinearRegression(Metrics, Diagnostics_plots,Data_plots):\n",
    "    \n",
    "    def __init__(self, fit_intercept=True):\n",
    "        self.coef_ = None\n",
    "        self.intercept_ = None\n",
    "        self._fit_intercept = fit_intercept\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return \"I am a Linear Regression model!\"\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        Fit model coefficients.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array \n",
    "        y: 1D numpy array\n",
    "        \"\"\"\n",
    "        \n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "        \n",
    "        # features and data\n",
    "        self.features_ = X\n",
    "        self.target_ = y\n",
    "        \n",
    "        # degrees of freedom of population dependent variable variance\n",
    "        self.dft_ = X.shape[0] - 1   \n",
    "        # degrees of freedom of population error variance\n",
    "        self.dfe_ = X.shape[0] - X.shape[1] - 1\n",
    "            \n",
    "        # add bias if fit_intercept is True\n",
    "        if self._fit_intercept:\n",
    "            X_biased = np.c_[np.ones(X.shape[0]), X]\n",
    "        else:\n",
    "            X_biased = X\n",
    "        \n",
    "        # closed form solution\n",
    "        xTx = np.dot(X_biased.T, X_biased)\n",
    "        inverse_xTx = np.linalg.inv(xTx)\n",
    "        xTy = np.dot(X_biased.T, y)\n",
    "        coef = np.dot(inverse_xTx, xTy)\n",
    "        \n",
    "        # set attributes\n",
    "        if self._fit_intercept:\n",
    "            self.intercept_ = coef[0]\n",
    "            self.coef_ = coef[1:]\n",
    "        else:\n",
    "            self.intercept_ = 0\n",
    "            self.coef_ = coef\n",
    "            \n",
    "        # Predicted/fitted y\n",
    "        self.fitted_ = np.dot(X,mlr.coef_) + mlr.intercept_\n",
    "        \n",
    "        # Residuals\n",
    "        residuals = self.target_ - self.fitted_\n",
    "        self.resid_ = residuals\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"Output model prediction.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array\n",
    "        \"\"\"\n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "        self.predicted_ = self.intercept_ + np.dot(X, self.coef_)\n",
    "        return self.predicted_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_samples=100\n",
    "num_dim = 5\n",
    "X = 10*np.random.random(size=(num_samples,num_dim))\n",
    "coeff = np.array([2,-3.5,1.2,4.1,-2.5])\n",
    "y = np.dot(coeff,X.T)+2*np.random.randn(num_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlr = MyLinearRegression()\n",
    "mlr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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z5s1HfcxA1pJ6K/AuEbkKyALygG8DBSLiV9UEUAbYg0NjhqlwOMxDq1axae1aGhsayC8oYNb8+cxbsACg223t1WC7O758ypQjzrVp7VqWZ7Ak66I1a06IgHEsBiyHoaqLVbVMVScAC4A/qOr1wGPA/NRuNwK/GqAmGmMGUE/5hAWXXsqNl17aY66hp+Nf2779iHzEsSzJOtwMxmq1nwNWichXgaeBHw5we4wx/SwcDrO0spI7fD4mpz0iKg2FuCovjzX//jcLVbnqvPMIpZZDLQ2FqCgq4qKWFj5ZUYFfhP/Nyjri+IqiIn4rwtLKSqrWr+/ojRzLkqzDzWAYVouqblbVOamfX1HVC1V1oqpeq6rRgW6fMaZ/PbRqFXOiUSbn5By5rbaWa4HpItRFIkdsn5yTQ1EkwtsikS6PB8hyHGZHo6xbvbrjtRN9edXjYVAEDGOMSbdp7Vqu6iafsCkS4Sq/n0K/nwO1tV3uU9/SwrTm5h7PMTsvj01r1nT8fqIvr3o8WMAwxgw6PeUTGhMJSkQIiJBMJLrc55DrUpBM9niOzvmIE3151ePBAoYxZtBpzyd0uc3vZ58qcVV8/q7TsCMchwafr8dzdJWPOJGXVz0eBmPS2xgzzM2aP5+NK1dS0cWciFlFRWysqeEqYHQ33/ZH5eTwTxHe0cM5NjQ1Maui4ojXT9TlVY8H62EYYwadnvIJ84qLWQNsUWVMFwFlW0sLkaIi/lRU1G0+os11h30+4lhYD8MYM+h05BMqK5kdiTA7L4/iQIDaeJwNTU20jh9PFVDX1HTEtg2hEF+57z6Abo93VId9PuJYWA/DGDMo9ZRPWPWHP/DAH/7QY66hp+NPnjhx2OcjjoX1MIwxA6an0h+lpaW95hN6yzV0d/yx1FEy1sMwxgwQKyU+9FjAMMb0u/TSHxVFRZSGQvhEOkp33OHzsbSy0hYtGmQsYBhj+l1PpT/AK+/RuXSHGXgWMIwx/a6n0h/tOpfuMAPPkt7GmKPSW6I6E40NDZQUF/e4T3EgQGNd3fFosjlOrIdhjMnY8UpU91T6o91wLyU+GFnAMMZk5Hgmqq2U+NBkAcMYk5HjmahuL/3xdH09u3ftYutTT/H0P/7B1qeeYveuXTxdX2+lOwYhCxjGmIwcbaI6HA6zfNky5s2YwaVnn828GTNYvmwZ4XCY0tJS5lZW8oHqah7cvZtc1+Ucv59c1+XB3bv5QHU1cysrrXTHIGMBwxiTkaNZ87q3XMfDDz/M+qoqvltezsiyMj7lOFyRSPApx2FkWRnfLS9nfVWVzcMYZGyUlDGmR+2jovbu2sXvd+xgbDDIqKIiioqLO9bTblcbjxMcMaLb9bjb19z+0C238O4RI7iktJRLRo2icvz4I867PRJh3erVVmZ8ELEehjGmW+k9hRsKC3nVcSgHnJoaqrdupampiWg02pGH+N6TT/Li9u1cFA4zsZvFjSbn5HDp/v3Eo9Eez23zMAYf62EYY7r01FNPcct73kNRQwM/UyXbcahJJJgowiXBIPnJJC8+/zx+x2GMKgkRngsGmRiP8862Nqq3buWU8nLyush7vE2Ve+rr4dRTuz2/zcMYfCxgGGOAwyfkvVZTQ8uePbzXdflgdjYljsNzsRi/jMe5ORbjYmBhMAixGFnBIBv8fjaI8P/Ky1n8wgu8KRSizXV5pbqa8ilTjnh0VRQIcCAe77E9Ng9j8LGAYYxhy5YtLK2sZE40yheysvhyXR2fSyY5Bag9dIhaVfyqzAfOB74E3BCLkQWUJhJcPX48VcXFlIZCHWtul/p8FMZi1EUijCsrO+x8ydGjSdTW9tim7pZQNQPHchjGDHOdJ+T9raGBua7LFKAIKHVd/KqUA28G5gA3ADHgdhG+AtyUChaQWnM7kQCg0O/nQBeB4R9ZWbQWFna7hOq2lhabhzEIWcAwZhhpnxvx8gsvdMyN+NTChVze0tIxIW9TJMJVfj8iQtR1GQ2MBZoAESEkwvuBCcBPVHFVqYtEOs4xr7iYhx2HbckkARGSqeDRbltLC4/k5PCVe+5hSTLJikiEcDRKwnUJR6OsiERYkkzaEqqD0FEFDBFxRKTnmTvGmEEpfcTTybw+N+LVTZso37WLplSpjsZEghIRRAQ/3nPrQuBA2nsVAwngMuBvPt9hvYjSUIjF5eUsUeX7sRj7HafLYDBnzpxul1BtX2bVDC695jBE5GfAQiAJPAnki8gyVf16XzfOGHN8pD92mlxUxGbH6agDlSXCBY7Dq6kEdXsOYqTrEgAU74MiASRVcYBaoACYDtzhulzaqRcxPS+PqilT+Owrr/DT7Gyy6+q8qrYVFVRdd11Hz6G3JVjN4JJJ0vtsVW0SkeuBjcDn8AKHBQxjhoiOOlBpE+na5fv9NAOFiQR1kQiziopYv2cPC1wXBxAgjvdhIXjfHNcDlwCOCLWq+LqYc1GfSNA8bhxr16+3R0sniEwCRkBEAsA1wN2qGhcR7eN2GWPegM5rVuzdtYsfFxYSzcs7YojrrKIiNtbU8H6/n+raWq6aNImFO3YwFS/JrcB+YFRq/+eBh4HlI0bQCtS2trJhxAgKo1GKAwFq43E2NDWxIRSyPMQJJpMcxg+AnUAO8CcROQUv/2WMGUTaE9qzpkzh7aedxoO33cYF4TAP5OczJpmkpLa2Y3Z2uvYkdbXrkkwkCDQ08JFAgNtFuBd4CdiL18v4IfAF4LMilKjyiCrZJSXowoWWhxgGeu1hqOp3ge+mvfSqiFzSd00yxhyt9nkUl7e0sHjXLi4IBGh2HDZGInxk/35EBJ/fz2mqvFJdjbpux7HtSerPv/gib1blkn37uCQYZIzPx/8cOsR3gJHAScBlItwtQqEIj7W1cW9WFktXrGDOnDlw++0Ddfmmn2SS9C4BvgaUquqVInI2cBHel41jJiLjgR/j/Tt0gXtV9TsiMhr4Od6ovZ3Adapa/0bOZcyJLD2hnZ9I4KrSlEhQH49zviqFwJdEeMB1WZyVRWEsxt4uktQzx43j6bPPZs3vfkeZCPl+P+eUlZE4cICrVZnuujjxOM+p8idVfu7z8a3Vq71gYYaFTHIYPwLuB5akfq/G+0B/QwEDb9DFp1T1KREZCTwpIo8CHwR+r6p3isjngc/jJdqNMV1IT2g//dxz+KJRxgDlIgRFOBt4wXX5flsbl4pwXiBAoou5EY/n5lK1fDm3zp/PcuiYiBeORllXW8sdkQiNPh/5fj8XjBrFxKIiCxbDTCY5jDGquhqvF4CqJvAGSrwhqlqjqk+lfj6Il0sbB1wNPJDa7QG8ZLsxphvtCxtFo1Fira2cJkKp4xBKzaUIiXCj43CK41ARjXJfPE7cdbudKNd5+dTSUIjK8eP55dSp/P7CC/nl1KmMyc1l7vXXD+BVm4GQSQ+jRUQK8QZLICJvARqPZyNEZALegIwtQImq1oAXVESk+Hiey5ihrvMIqF07dxIfO5aaZJIiYEQXxxQDAeADwSCbQiFmpRLU+QUFTLv2Wi5U5c5PfILGhgayR4ygpqGB0/1+Lhk16oj3ai/bUWVlO4YdUe15hKyITAW+B5wDPItXXma+qj5zXBogkgv8EbhDVX8pIg2qWpC2vV5Vj/hXKyI3AzcDlJSUnL9q1apez9Xc3Exubu7xaPaQZfdgaN+DlpYW9r72Gvmq5DsOAcdhe0sLpaqgSkAEvyoi4h2giuLVfXoNL2G4S4SxEyYwavToLt8v7rrUJxLUxePkBwIU+/0ERIir0ui6NIpw0sknk9PN2t5DwVD+N3C8NDc3M3fu3CdVdVqmx2QySuopEXk7cAbevJ0XVbXnusQZSs3v+AXwoKr+MvXyPhEZm+pdjMWbVNpVu+4F7gWYNm2azpw5s9fzbd68mUz2O5HZPRi69yAcDlM5d643Wzvtw3rbrl2Ea2p4V1sbe4HxQJ4ICrQlkwTwEpEJEd4OvBX42N13EwgGue/22494v3ZP19ezaP9+8k46idihQ95M7Wuv5Zq0mdpD1VD9N3A8bd68+aiPyWSU1Ac6vTRVRFDVHx/12Q5/X8FLnD+vqsvSNv0auBG4M/X3r97IeYw5UXQ3W3tecTGV+/bxJqAUOBgIEIjHUdclG3gBr0TDt4F9IpyWnU2Z4/D/Fi7kQ7m5TB47tsvzvXnUKD6YSBC//nor3WGAzJLeF6T9+Q+8UvjvOg7nfivwfuBSEflX6s9VeIHinSLyEvDO1O/GDFvtE/KW3347J7/8Mlufeordu3YRTS1x2j6P4na/n1+o8lI8DoEAEeA+YDHwccehNCuLP4RCXH7SSWQ5DlkHDnChLZNqjkImj6Q+kv67iOQDP3mjJ1bVv+A94urKO97o+xtzIkhf2GhMMsmlwSBJYH9NDdX79nUsgTo9L4/lU6aw4pln+F4iQXE8TpHjcFkwyA+CQUpT5cZ/o0pVcTHVeP/z+/bvhwkTuj2/LZNq0h3LinuHgEnHuyHGmMN1XlO7pq2N22IxnlflIJALTHn2WSqmTOHUkSM5deRIbjzjDNbt2MHb4nEWhUKUOA61qqyIxdjgOCwuL6c0FKIaGO33U2vLpJqjkEkOYz2pIbV4j7DOBlb3ZaOMOVF1HhKbX1DArPnzmbdgwWGJ5C1btvCJ+fNZUFfHDaEQO5NJPgHkJ5N8AzjZcdgvwq/icW599llunzyZ6Xl5/EmV93/mM6y9/3627t9PazxOvt/PrLFjO5ZQbXfh6NH8ubaWy3pory2TatJl0sP4RtrPCeBVVd3dR+0x5oSV/nhpeV4eJcXF7IvF2LhyJZUPPsjiqiqmT59OOBzmixUVTNm3j8eTSR6Ox6lzXa4GLgRKgLjrcpLPx4cdh2nxOHdUV/Ox00/35kd86EPkjRxJaOVKKrooZ94uEAqxobCQBWmr7aWz+Rams16T3qr6x7Q/j1uwMObodV43uzQU6ljAqKKoiDt8PpZWVhIOh/nWXXfRsn075yQSVOEtg/pT4HS8kU7r8cqNt6QKCJ4OXBSP86n9+ztma89bsICHQ6Fu18xuc10ez81lsS2Tao5Ctz0METnI64+iDtsEqKraUq3GZKinBYwAJufkMDsS4YEVK/jtD3/Ij30+JjkOI4BtrstkYKoIF6myGC+JuFeVoCoxEWaNHcufx4zpKCdeWlrK4qoqllRWMjsSYXZe3mFrVTiqHT2aqVOnsm71ahatWUNjNyvjGQM9BAxVHdmfDTHmRLZp7VqW5/X8HWt2Xh7X3HcfV7W14QdedF1UlTgQBeKqnA7MBp4Q4a0iFGdloaWllJSW0tppNNP06dOpWr++y2AwaeLEw4KLLZNqMpHxKKlUTaes9t9V9bU+aZExJ6DGhgZKinsui5bV1sa+PXu4Bq+2f3tZj5fwkoe5eF3+S4FPqlIOrEkkeHbvXur37KHO52P5smWHJdC7CwbHMsvXmF5zGCLyrtQkuh14NZ92Ar/p43YZM+S1T7ibN2MGu3fuZPOTTx424S5dNBrl6RdfJACcmaoLlY23cFEJ3lj2GN43vPF4/xP+ryonOw73OA4PAj8ZM4bQypVUzp3Lli1b+usyzTCSyUzv/wHeAlSr6ql4k+oe79NWGTPEbdmyhcq5cwmtXMly4CNjx1KdTOLU1HS5TGqktpZ/JhKMUOUAqUl1eAnDIqAeb4nUOF4FUAXuDAT4cFYW+ao0+HycM3bsEQl0Y46nTB5JxVV1v4g4IuKo6mMicleft8yYQaa7ORQXzZzJ3zZv7ng9OGIETfv2sbywkDenktzvKSmhsraWi1Upi8d54V//wh8KEQgEGFVUxD/CYTapcpXfz28SCW7BW3TGAYLAyXi9ipF449xvAE5zXcKxGPsdh1PKywml5li0J9DXrV5teQlzXGXSw2hIlSD/E/CgiHwH75GqMcNG5x7Do8XFLAf23H03N158Mcnly1kOPJCfT/bLL5PYs4dFzzzDu554guW7dgHw0XHj+HRrK/8Xi+FTZbTrkuu6PLh7N59ta+N6x+HDWVn8Gm81MV/q3EkgB5iIt2DMFuDNIlSroqWllE+ZQl6nhLrVgDJ9IZMextVAK/AJ4HogH/hKXzbKmMEkfQ5F52Gxz0UirBQhq7aWl0eM4Js7dzKlpYVlIowDXm1t5bFwmIV790fQ9HwAACAASURBVPJe12V5djaPJJPcHovxYizGGLzcREKVH0Wj7EokuBz4JDAXb0RUGVAHrAPuAUaL8B+5ubwswriysi7bbDWgTF/IJGDcDKxJTdh7oLedjTnRdDeH4qHaWua4LhcEgzzV1sbSl17i68EgMREmpJZHPdl1uRGYFo/zhXicS0aOpDIQ4N2Ow4xDh5gVi/EfQB5wEHg+meRRYCGwG/goXv5iDN7jqOuCQf6SSPBCPE5JN8ECrAaU6RuZPJLKA34nIn8WkVtFpKSvG2XMYNK+ZvYRr0ciXOX3vnP9WZWrYjEm+3z4RYil9gmIEI/FKE8meRewLhajLh7nyUOHOBW4zXF4h+NQjjdj+23AEuB+YB7wsAiPAB8E6h2H2Y7DmSNH8hAwpoeyHxuamph17bXH6xYYA2RW3vzLwJdF5FzgvcAfRWS3qs7q89YZMwjURiL8wnX5Q10d++Nx4q5LDKiLRqn0+XhnIMAj8TjfAlzXZaQIkUSCk1LHu6nexhwRbo3FuECVrcB7RAiJ0Oa65OB9MzuA17OYBfwceI8qjwA/Bs4W4ZfAs2PH4qiyPZFgcloxwXZWA8r0laMpb14L7MUrY9PzDCRjThBbtmyhqaYGv+typwit0SiuKluAh4AbXZf9sRjVrkuOCIdaWihUZQdQAGQDooqqMkaEA8AOVf4MrHC8Dn7CdRmBN4R2FK+vH/BxYLMIbwIWqvJFVa5buJCffOYz7Nq1q9uyHxtCIasBZfpEJuXNb8HrWRQBa4EPqepzfd0wYwZae7L726NHM6mujsZolEki5DgOU4HpySRfUGWF47Aa2KbKxaqMcBwmiLAjmSQPL0ntxwsUh4DbgR86DqXirR+mpAq0AT7H4SQRDiSTFDkOX83Kwuf3k1dUxMk+H3d961uAN4O7u7IfVgPK9JVMehinAB9X1X/1dWOMGUzak90Xl5XxzL59FKlSD7ySTJIAAsBFwA+SSS4HngYmAyOAPBHKHIftqjQ6DklVfqZKGJiSCijtBHBJBQwRgkCdCCW5uZw3dSoA4WiUQjl8gUqrAWX6WyY5jM/3R0OMGWzaCwaGQiHEcWhwXYrwHhcF8SbSNQOr8EYwJYHxrssVQBuwX4QzRowgz+9nWzLJP+Nx/NEoUx2Hh5NJPuQ4Xu9ChJgqIZ+v4+c/AbPSktq2kJEZDDIZJWXMsNTY0EBJMEg0GiUZi3F66nGRH/gb3sSk04Af4CWlvwJ8C1jqulQ7DqeNGEGz47AiFmOJKl8980yyCwtxRo3iFyI84bocAnyhEMlU8AD4q+vyWCDANalihe1J7GssiW0G2LGs6W3MsJBfUMC+WAy3tpYiVbJUEbyRH/8L3IE3+/olYHTqz53Ao8B/JxKMjccpCQY7lkcFOG3CBLa5Lh8cM4av7drFbNdltt/PaJ+PV1pbedh1+aEId0yaBMCKSMSS2GbQsIBhTBfC4TBZY8Ywf9MmaGujUJV3ANfhjY6aA5wDhPFGNo3CK+dxZmpexahkkn/n5PCzKVM63nNFJMLcigrOv+gillZW8tbSUuqiUW6tr+eA6xIPBtkfCjF67Fi+q0q+iCWxzaByLCvuAWAr7pkTVfva25e3tFAuwlmqJIGNwC14M6//D2jBG2Nejpe4juEFDoDrRHigvr7jPdPnRqSPcNq0Zg1NOTkUFhQw69prucaCgxnEel1xT0S+gtcL/wne/xfX4+X4jDnhHFY3auxYmnJyeO6ppygDbgSmA+/D61kcwFubArzkt+BN0ou6LqVAVJVwNNrl3Agb4WSGokweSV2uqtPTfr9HRLbgPcY1ZshLL1u+4+WXGXHwIJtLSxnl91Oalwd+P03JJPWpFfDmAKuBz/F6z6IJb7TTiJwc4vE4L0ejJIFF9ljJnEAyCRhJEbkeb/Sg4n3BSvZpq4zpJw8//DBfu+UW3rF/P7eokhuLUej388dwmMp9+1hcXk52VhaJQ4c43XEIAe9OJvkUXpJb8HoXBwBUaXZd8kIh1rsuM668kp899NAAXp0xx1cmAeM/ge+k/ijeanv/2ZeNMqY/PPzwwyy+7jq+q8qMYJCgCE/HYpySSPCfySTTg0Fur67mtsJCRicSvByLkeu6nIpXJ+fvwEl4OY1SIEuEV1tbORgM8lPH4YEvfGEgL8+Y4y6TiXs78dbEMOaEEQ6H+eLChXwYuCQrq+P1gAiCVwPq9FiMK4NB/gpc4vfjSySIuC4v4yW8bwBy8dbcPh+4QpW/JpM8kEjw2XvuYWpqlrYxJ4peJ+6JSLmI/F5Enk39fq6I3Nb3TTOmb8TjcT61cCESDnNZWxvNBw/S1taG67qMCgTYr4pPhIAql6myub6esaeeysFEghZgOV5hwE3Ar4HP4z2a+hDwdE4O4886i5tuumngLtCYPpLJTO8VwGK89edR1WeABX3ZKGOOp3A4zPJly5g3Ywbnn3oqLz37LC//9rdkA6eJeJViYzEONTcz0nWJuC5NiQSO61LQ1kZDPE7boUMocBfwKeAjeOtsn+w4XOrzsdTv514R9sTjtLW2DuDVGtN3MgkYI1T1H51eszW9zZCQvhb3F2IxyurqOBXISiQYpcpewBEhIELQdXFiMU52HF7Fy1PsAQLRKM+FwywDLgRm+XyM8Pvx+/04joOkigLOcBwuicdpjccH6nKN6VOZBIw6ETmd1CQ+EZkP1PRpq4w5DtLnVFQUFfG3hgbmui7ZIhQ6DtOBDa6Lq4qbTBLAKyo4Aij3+UiI8GNgp+Nwc1sbO30+3i9CXqeqse0CwFtVOXJJI2NODJmMkroVuBc4U0T2ADvwJu/1KRG5Am9klg+4T1Xv7OtzmqErfS5FY0MD+QUFZI0Zw5SGBjaLcNuLL/JsczPlwAddl2l+PyHXZYMqFyWTnIM3jyKgyiHXJdfv5xXg8WCQx84/n/n/+hcaj1MiQjKV4+isLVXOIxAM9vflG9MvMgkYqqqzRCQHcFT1oIic2peNEhEfXm7xnXgrVj4hIr+2hZtMV9pLecyJRlmel0dJcTH7YjHufuQRHorHeVcgwPJQiLAqY0X4myp/jcc5KMKHgSWqzAXmqFKEN4v7d8kk94qwtLyc0lCIOcXF/OK112jMzsYXjRJw3Y4RVQrEU4+35KSTKBwzZgDvhjF9J5NHUr8AUNUWVT2Yem1t3zUJ8B4Vb1fVV1Q1hjdp0Ib2GuD1JPYV06ZRPmYMN8yYwYJnnuHCV1/Fra0lEYtRCFwdj3M/sC0epy0aJaDKKNdlDLBUlaAqPwYuAOrwutKX4k0yuj8QYOnZZzMn9eF/w0knEfH5+LUqI3JyIBTiENCcWkXvUCBAc3Y2/8rLY9a11w7EbTGmz4lq1/UFReRMvAXE/hf4TNqmPOAzqjq5zxrl5UmuUNWK1O/vB6ar6qK0fW4GbgYoKSk5f9WqVb2+b3NzM7m5uX3T6CFiKN+DeDxO7d69NNTVdayT7cObcV2Et2qdK0JCBMfnQ+JxgkAErxfQXhgwWlZG7u7dRICECK4qbXjlCxQI+v2UhUIEnNe/T8Vdl5eTSdxYjPGq5DpOR+8ioUoCcINB9joOJ0+cSCAQ6K/bckyG8r+D42G4Xz9492Du3LlPquq0TI/p6ZHUGXhlcwqAuWmvH8Qbct6XusoqHhbZVPVevNwK06ZN05kzZ/b6pps3byaT/U5kQ/UebNmyha9/9KNMfekl3idCLBolF/iA6/I/wPk+HzlAK6BZWTzX2sqZfj/BWIwDeMNg/8/nozqZJPKNb/D2T3+aPXjfOG5zHE4CGvx+vhQI8P1zz6U0dHjqekUkgltRwcmTJrH0llu4ZP9+3qZKUSCAW1jIllCIR3JyWFxVxfTp0xnshuq/g+NluF8/ePfgaPVUrfZXwK9E5G2q+qf0bSLy1qM+09HZzeuFQAHK8B4tm2GofbTTJxsaOMtxcF0XByh1HLJclzLgrmSSZ/GKAOYdOsSZqtyUTDIWGAM0AiERTvH52I33jykPaADw+fhhIsHf43H+2+cjNxqFtIDRuTT51KlTWbd6Nd9fs6YjwT7r2mutwKA54WWS9P420LnGwfe6eO14egKYlEqu78GbKGj1q4aph1atYk40SnFjI4V+P9WpdSrAe4z0SbyucAXet4ywKr8CPplM8nkRSlTJxis3nos3dLYReBpvrkVFMsnccePwHzjAAVWeeOEF3nLuuTSKWGlyY9L0tIDSRcAMoEhEPpm2KQ9vqGufUdWEiCwCfpc610pV3daX5zSD16a1a1mel8e+RIJgIEBClaAIe1yXOPBp4G14j6N8eIUAP+Q4XOS6fFWVi4AZfj/VqsRdlxiQ7zi8psrHQiHWOQ53TZxIOBplXW0tXwyHObRjB6eefrqVJjcmTU89jCBebTU/hy+Y1ATM78tGAajqRrxFzsww19jQQElxMfv9fmKq+EWIqvIL1+X9eI+cWvGSXO2JLkeVScBMYCWw0uejPBQi3tJCE9AgwiMifNHv57FUcrs0FKJy/HiuKS5mkQi/fPzxfr9WYwaznnIYfwT+KCI/UtVX+7FNxhwmv6CAfbEYo4qK2F9TQ4HfT100ymN4k3VygVd4/duNH28S3QFgUup3JxbjuXgccV3q8J6zLs7O5knXZVZJyWHnKw4EaKyr68crNGZo6OmR1LdV9ePA3SJyxNhbVX1Xn7bMmJRZ8+fzsx/8gEAiwca2Nlpdl2y8dYML8Ep5FOB1fV/Ey2skgDE+H+e5Li7wVcehVpUI8CURqnJyqFdlg+NQVVx82Plq43HyCwr68xKNGRJ6eiT1k9Tf3+iPhhjTnVPKy1m8axcfBlZkZTHy0CEO4NWneRyYiBc82udFgLeeRa4q0exsStrauDsUotDv577WVlxgQyLBBsdhcWomd7oNTU3Mqqjot+szZqjoKWBEoOPRlDH9Jr0u1P66Omp372aWz8eElhb2AfvxJuFdDfwTL8F2Et7kvSy8OvwR4FXX5bFEgtnjx6OBANW1tUzy+XhRldrCQqrKyo4IFulDaI0xh+spYKwjNXRWRH6hqu/pnyaZ4aRz0UDH5+NAfT3/mZPD8sJCmlta2N/Wxr+Ae0X4tN/P2bEYTcA4vPUp3o43MsqX+uPHG15bA/wwFuN7eXmMKyxkXFkZZ7kur4RCbC0qYkNTE7Pz8igOBKiNx7scQmuMeV1PASN9tvVpfd0QM3y0B4nV999PZPt23iPCkuJiJhYW8uQLL/C8KpsaG5keDBLcu5dzgQtUmaHKbbEY3/L5KFNln+tyBXAnXpXKq4GxeL2Lh4ENqdcfCIe5vLAQ8PITgUCAqvXrWbd6NYvWrKGxrs6bfGdDaI3pUU8BQ7v52Zhj1l5Z9uLmZnL37GG5388kx2F/JMJLNTWc4Ti8IyuLS5JJPv3iiyxyXbLwqmSei1ejZmMyyQeBP+PVv1fgPuCjeInvAuASoMpxiAGz6+s7zr+hqYmCUaNs8p0xx6CngPEmEWnC62lkp34m9buqal6ft86cUNIXNNqcTDJPlXNSRfpKg0EibW3k4BXxO0uEK+Jx/grMwivrAV4J8kVApePQ6LqMA1qA9wFn+Xy0JpOEAL/PhyNCzHWJpgpstucnbh016oi2GWN61215c1X1qWqeqo5UVX/q5/bfLViYo9Ze4mNyTg6bIhGu8r/+fSXqusRU8bkuzQcP0nzwIFcA/8ZLcrcrxivr4Xcc8vFKexwAckRoA3yhEDHHIa6Kq8puICjCikiEJckki6uqBn0lWWMGq0zWwzDmuNi0di1X5XnfNRoTCUpSvYamRILqlhYcVQLACFUcVYqBZryA0ZLqJdQC+an3uxT4ZWr7uJEjyR05khHZ2YzIze1Yr+JB1yWan0+8ooKq9euHRCVZYwarTIoPGgN0vQzqrPnzmbdgQUaJ4vYSHwD5fj/7VClU5dXWVk4D6h2HetelGC8vUY83e7sMbyZ3oSrrgEtECKsSxSv7McHv58K0tSscxyEUCvGMCKtVWbtpE1On9mWtTGOGB+thmIxs2bKFyrlzCa1cyXLg0eJilgOhlSupnDuXLVu29Poe7SU+AGYVFbExkSASi1GoSo4IRSLUAYfwEmWPAOfhfauZ6Di8IMI9wM+BRUBjIMChQIDFfj93tbayI5Eg5rrsSCS4q7WV/1bl49/7ngULY44TCximV+nJ6oqiIkpDIXwilIZCVBQVcYfPx9LKSsLhnpcsmTV/PhubmghHozQkEnynrY0/tbVR57rsdl0ASkXYIcLf8B43vU+ELcB3XJdvivD9nBxWjxzJlaEQf/L5WHDrrfzk8cf595VXMluEs9ramC3Cv6+8kgf+8hduuummPr8/xgwX9kjK9KojWV1U1OX2yTk5zI5EWLd6dY/DVOctWMCCe+9lzb//zbXA0qwsvnPoEPOAt7gutYAjwuOhED9JJtmXSPBhEQpEcAEdMYIvui75wOTCQnLy8/nEZz5DaWkpP3vooT64cmNMOgsYplft61H0ZHZeHovWrOl1XkMIWKjKdBEK/X6yHYetqnxFlVq8vMUHSkr4ZWkpu6JRllZXc1kyyRk+H2+fOvWwGdlfsRnZxvQrCximW+1J7mefeoqrXJdYMon4fIwPhZhbUsK84uKOWkyZlAR/aNUq3uv3c9V551EXiVBdW8tIn493JZPckpVFIBDgh4kEcb+f0lCI0lCIqilT+Owrr/DT7GyybUa2MQPKAobpUvuM7LfV1fHNaJST8L79/8V1eSSR4MVdu6jct4/F5eVMz8vLqCR4e08lFAoxrqyMcWVlRKNRqrduJSFCyHGY7fezKBKhcry3pHt9IkHzuHGsXb/eAoQxA8yS3uYI7UnuTx88yMW7d/NmEaYAlwG3AXeq8lgsxmWJBEurqwlHo15J8Guv7fF9GxsaKAkGD3stFApxSnk5r6gSjsXIV6UhkSAcjR422c6ChTEDzwKGOcJDq1ZxeUsL2a+9xiSgzHFonxsdEuESEW4G7otGeWsiwT27d7MhFOKaXkqCpw+rTZeXl0f5lCloaSl/V2WPKotEbLKdMYOMPZIyR9i0di1L2toYFY+TJcIeVerxVrHzqzIKmA38nyr7Ewl+feAA969Z02svYNb8+WxcuZKKLkZbtT+m2hgK8bGKCisKaMwgZD0Mc4TGhgb8Bw4QVGV7MonjukwC3oS3RrYADXgLFT2ZTJI7enRGvYB5CxbwcCjEtpaWLre3FwfsradijBkYFjBMh3A4zPJly9i7axevHTxIWJVxQAnecFgHb0W7cUAeXp2nRlWaDxzoddIeQGlpKYurqliSTLIiEiEcjZJwXctXGDNE2COpYapzXaikz0e8oYH35+RwQ2EhzzQ3MxXIAVrxAkX6P5bHgDcDTwIX5Ob2Ommv3fTp023xImOGKAsYw1D7kNk50SjL8/Jozc5m4datLE4kGCVCYyjEJ4AreX3p0zZgBF4vYxveanYfBraI8MmyMr6awaS9drZ4kTFDkwWMYSYej/PtT32KO3w+JhcV0dTUxPJnn+XdiQSXOQ6HgFeiUfzAl4A5eAnuUcBOYBNesFiE1/soDAY5Nyen10l7xpihzwLGMNNw4EBHXahoNMqr1dVsTSS41XFwRMgFxqhSJsLnVPk9cBNevmIEcDHwGWCECMGsLMoCgYwm7Rljhj4LGMNMU319xyJGkdpaCl2XZrzEdrtCEUpcl9/hPXb6DBDAW+muCWgQ4eQRI3hIlVlFRd6kvYqKfr4SY0x/s1FSw0wymeyYbV0fiVDo95Mvwr60fSKqbAf+gVcOJMjrI6RO8/s5w3H4c2sr60Q4KzfXhsIaM0xYwBhmfD5fx2zrRCJBUIRZgQAbU0ugAvxCleuAr/h8fAG4Dy9/kQR2uy4PqvINVZKhEN/x+WworDHDhAWME0T7HIp5M2Zw6dlnM2/GDJYvW3bE/Ii8UaPY2NQEgN/vJ6bKvGCQh0XYlgoaj6gyW4TpItztODQClY7D3ECAm4CdPh8fzc6mKS/PSncYM4xYDuME0HmYbElxMftiMTauXEnlgw+yuKqq40O9YPRoHgyFuKilhVFFReyvqaE0GGRxdjZLWlt5RzJJHTBOhKgqSVXeGQpxy+TJ5KWtiZFwXe6tq7OehTHDiPUwhrijXT41EAh0zLb+td/P80BjIsF4x+GtgQD3BwI0OA6bVXlBldpgkEnnnHNYsABsZJQxw9CABAwR+bqIvCAiz4jIQyJSkLZtsYhsF5EXReTygWjfUNKxfGpOTpfbJ+fkMDsaZd3q1R2vtc+2ZuFC7jz9dC5IJLg2HidRUsK6N7+ZG8eP51/BIM6IEZwxeTIjR4484n0zKWdujDmxDFQP41HgHFU9F6gGFgOIyNnAAmAycAVQJSK+AWrjkLBp7dqOYbLdmZ2Xx6Y1aw57rX229aPPPMPml1/m+q99jSfGjeO/Dh7kz0VF/KqoiMTppx/RswArEmjMcDUgAUNVH1HVROrXvwNlqZ+vBlapalRVdwDbgQsHoo1DRVeLEnVWHAjQ2NDQ7fb24PHLxx/n99u28ZsnnmDZ2rV82eezIoHGmA6iacMpB6QBIuuBn6vqT0XkbuDvqvrT1LYfAr9R1bVdHHczcDNASUnJ+atWrer1XM3NzeTm5h7X9g+0l194gZOBgNN97I+7Lq+JcPoZZxzVPYjH4zTU19NUX08ykcDn95M3ahQFo0YRCAR6f4NB6kT8d3C0hvs9GO7XD949mDt37pOqOi3TY/pslJSIbAJO6mLTElX9VWqfJXjr8jzYflgX+3cZ0VT1XuBegGnTpunMmTN7bdPmzZvJZL+hZNtTT/HvH/yA7GSSTZEIjYkE+X4/s4qKmFdcTGkoxIpIhHhFBTNnzjwh78HRsntg92C4Xz949+Bo9VnAUNVZPW0XkRvxatu9Q1/v5uwGxqftVgb0vtDCCa5zKfL8ggJmzZ/PvAULOKW8nMW7dvFhYHkgQEkgwD5VNtbUULlvH+8dP54N2dlUWb7BGPMGDcg8DBG5Avgc8HZVPZS26dfAz0RkGV5l7Ul4FSqGrZ7mWPzX/ffjxuPcN2ECI3btgkSChN/PSSK83+/n9FiMj+7cydLVqy3fYIx5wwZq4t7deIu4PSoi4OUtFqrqNhFZDTyH96jqVlVNDlAb+1RPvYb2D/f0ORaT09bBbp9jsfPll5FIhPPe/GYoKKAuEqG6tpZkPI7P76e8rIxbfD5e2759oC7TGHMCGZCAoaoTe9h2B3BHPzan3/U2M7viy1/m1epqfvrd7xKtqeG2rKzDchLttjU28gWgLhJhXFlZx59010SjLDqKxY2MMaY7NtO7n/U2M/u9ra0svu46nHvv5av79/OXUIjlQKimhsqtW9mSqgMF3gztMwMBDtTWdnu+3obUGmNMpqyWVD/rmJmdesQUjUaJ1NZSH4lQE4vxo1iMZY5DeTTKAdclOxBghAgVwSAXJZMsqa6masoUSkMh8v1+DqiSTCS6PZ+V8DDGHC/Ww+hn6TOzm5qaqN66FaemhnK8Ke/vUeUtrsve114DEWJp82Qm+3zMdl3WpXoUs4qKWJ+aH9EdK+FhjDleLGD0s/aZ2e3Lo54mQmkwSMhx+H08zhzHIUeEAOAmEuyLxw87frbfz6ZIBIB5xcU8qEokP7/Lc1kJD2PM8WSPpPpB+oioXTt38q5du5gRCnFlIkFOVlbHfo2qlIgQBwIijHYcwq5LYTJJjs8rqVUsQmMqiNQnEjinnMI3g0FejkSYnZdHcWqN7Q1NTWwIhayEhzHmuLGA0cc6j4iKjx3L/vD/b+/+g6wq7zuOvz/768oss4Cwu2VlG20FrIhRQ12tnZapNBoVG6aU0HZSY12TdiGRmTY1xHZqp2aSxk6tGdlkJJLRDA2/YoyrNkZsnDRJ3YQo8RfGWgyNgrKLAmUld399+8c5K9flLtxl9u7d7P28Zhju+XXP8zwc9rvnOed5vnv5QU8Pt0rcUlVFS9ql9E6q1AhOr6lhVnU1XQMD7I5gZm8vM6uq6IpgSkUF67u6eDiT4fZ776W5uZkHtmxh9datHOruTl7RbW2lfcUKBwszGzMOGEWUbxxFtrGRw/v3s6K3l8XA3x49SnttLU0VFSyprub+bJbfrahgXk0N1RJEMO/8898ZY/G1bJZ9TU30DQsIbWvW+NVZMysqB4wiGv5GFEAmk+E98+bx4s6dnAG8f3CQr/f2sqyqikP9/WyMYOHAAH09PWSrqqioqSGTyXDGnDkcnDGDFwYG6Ojo8J2DmY07B4wi2r5tG+vy5JOoq6ujobmZQ3v3Mn9ggL/MZnmot5fFlZXcNmUKd2SzfGBwkAuzWQYi+Gl3N9+N8DMJMyspB4wiOnTwII0NDXm3zW5q4qUDB7hocJD6o0e5v7b2nQfbLdXVfDGb5c6+PuokDr/yCh/+5Cdpv/FGBwszKxkHjCKaNn06b/T2vms6jyFDXVM/eO45+oFDEpkI9kfwcH8/z1ZX85UFC2ipq0umJ582zcHCzErK4zCKaMny5TySM5XHcHV1dfxnRQVN06axGriir4/VQN/s2bQvXEhL2p2VL8Wqmdl48x1GES1buZK2jRu5tKeHBbW1x21/vqeHbwCPnnMOzTnjMYZrqK7mUHd3EUtqZnZyvsMooqamJta2t3PLwMCIubHrzz6bSuVLNHiM54Mys4nAAaPIWlpaaO/ooK+1ldUSV3R3s1pKxlF0dLDi+utP2G0Fng/KzCYGd0mNg6amphEH1hXSbfVwJuMUq2ZWcr7DKLFCuq089sLMJgIHjAngZN1WLS0tpS6imZm7pCaKE3VbmZlNBL7DMDOzgjhgmJlZQRwwzMysIA4YZmZWEAcMMzMriN+SGiY3//ahgweTdKfLl7Ns5UqPhTCzsuY7jBydnZ20LV1KZsMG1gGPNTSwDshs2EDb0qV0dnaWuohmZiXjgJHKzb/dWl9PUyZDpURTJkNrfT2fqazks21t7N27t9RFNTMrCQeM1Dv5mBx7kwAACOhJREFUt/PM5wSwoLaWq7NZHtiyZZxLZmY2MZTlM4x8zyle37uXf5s164THXV1Xx+qtWz0a28zKUtkFjM7OTj7b1sY12Szr6upobGjgjd5ertm3j0Pd3RyeP5+6NNPdcE5kZGblrKy6pPr6+kZ8TvGe005jKrDnpZfIZrN5j3ciIzMrZyUNGJL+WlJImpUuS9IXJL0s6RlJF43l+Q6++eaIzymW1NfzRAQzBwfp7urKe7wTGZlZOStZwJDUDPw+8L85qz8AzE3/fBT44lie8/Bbb3HVCN1NyxoaeKiigjck3ty//7jtQ4mMPuhERmZWpkp5h3EH8DdA5Kz7A+C+SDwJTJc0e6xOODAwQGNNTd5tTZkMa+fN41aJr/3iF05kZGY2jCLi5HuN9Umla4HLI+ImST8DFkVEt6SHgM9FxPfS/R4Hbo6IHXm+46MkdyE0Nja+b9OmTSc9b3dXF9MOHKC6YuQ4+fbAAHv6+6mpqWGgv5/KqirqZsxg+owZVFdXn0p1J5QjR44wderUUhejpNwGboNyrz8kbbB06dIfR8SiQo8p2ltSkrYDv5Jn0y3Ap4H35zssz7q8ES0i7gbuBli0aFEsXrz4pGXaunkze+66i9b6+hH3Wd/VRbS2csPHPnbS7/tl9MQTT1BIW01mbgO3QbnXH5I2GK2iBYyIWJJvvaSFwFnATyQBzAGeknQx8CrQnLP7HGDMhlZPP/10NmYyXNrTk/fB99BzinY/pzAzO864P8OIiGcjoiEizoyIM0mCxEUR8TrwIPBn6dtSlwCHImLfWJ27urqate3t3DIwwPquLj+nMDMbhYk2DuMRYDfwMrAeaBvrE7S0tNDe0UFfayurJa7o7ma1RF9rK+0dHbS0tIz1Kc3MJoWSj/RO7zKGPgewqtjnbGpqom3NGk/xYWY2ChPtDsPMzCYoBwwzMyuIA4aZmRWkJAP3xpqkLmBPAbvOAsp9ulm3gdsA3AblXn9I2qA2IkYemDbMpAgYhZK0YzSjGicjt4HbANwG5V5/OLU2cJeUmZkVxAHDzMwKUm4B4+5SF2ACcBu4DcBtUO71h1Nog7J6hmFmZqeu3O4wzMzsFDlgmJlZQcoqYIx3DvGJQtLtkl5M6/gNSdNztq1N6/9TSVeUspzFJunKtJ4vS/pUqcszHiQ1S/qOpF2Snpd0U7r+dEmPSfrv9O8ZpS5rsUmqlPR0mqgNSWdJ6kzbYLOk/Ok4JwlJ0yVtS38W7JJ06Wivg7IJGKXIIT6BPAacFxHnAy8BawEknQusBBYAVwLtkipLVsoiSuu1juTf/Fzgj9P6T3b9wF9FxG8AlwCr0np/Cng8IuYCj6fLk91NwK6c5X8C7kjb4C3ghpKUavzcCXwrIs4B3kvSFqO6DsomYFCCHOITRUR8OyL608UnSRJTQVL/TRGRjYhXSKaVv7gUZRwHFwMvR8TuiOgFNpHUf1KLiH0R8VT6+f9IfkicQVL3e9Pd7gU+WJoSjg9Jc4CrgS+nywJ+D9iW7jKp20BSHfA7wD0AEdEbEQcZ5XVQFgEjzSH+WkT8ZNimM4Cf5yy/mq6bzP4c+Pf0cznVv5zqmpekM4ELgU6gcSg5Wfp3Q+lKNi7+leQXxsF0eSZwMOcXqcl+Pfwa0AV8Je2W+7KkWkZ5HZQ8H8ZYKXYO8YnuRPWPiG+m+9xC0kWxceiwPPv/Uta/AOVU1+NImgp8HVgTEYfT9MhlQdI1wP6I+LGkxUOr8+w6ma+HKuAi4OMR0SnpTk6hG3LSBIyJmEN8PI1U/yGSrgOuAS6PY4NvJk39C1BOdX0XSdUkwWJjRNyfrn5D0uyI2Jd2w+4vXQmL7jLgWklXAacBdSR3HNMlVaV3GZP9engVeDUiOtPlbSQBY1TXwaTvkiplDvGJQtKVwM3AtRHxds6mB4GVkjKSziJ5+P/DUpRxHPwImJu+GVND8rD/wRKXqejSvvp7gF0R8S85mx4Erks/Xwd8c7zLNl4iYm1EzEn//68E/iMi/hT4DrA83W2yt8HrwM8lzU9XXQ68wCivg0lzh3GKHgGuInnY+zZwfWmLUzR3ARngsfQu68mI+IuIeF7SFpILpx9YFREDJSxn0UREv6TVwKNAJbAhIp4vcbHGw2XAh4FnJe1M130a+BywRdINJG8O/lGJyldKNwObJN0GPE36QHgS+ziwMf2FaTfJz7sKRnEdeGoQMzMryKTvkjIzs7HhgGFmZgVxwDAzs4I4YJiZWUEcMMzMrCAOGFY2JM2UtDP987qk13KWSzZTqaQlkh4o1fnNClXu4zCsjETEAeACAEm3Akci4p9z90kHuikiBo//BrPy5jsMK3uSzpb0nKQvAU8BzZIO5mxfKWloltNGSfdL2iHph+kMAcO/b0fOiFokfU/SeyVdIum/0snfvi9pbp5jb5O0Jmf5xXSmVSRdl55zp6R2SRWSqiR9VdKzaR0+MbatY3aMA4ZZ4lzgnoi4EHjtBPt9Afh8RCwCVpBOlz3M5nTb0LTaM9OZkncBv52e4x+B2wotnKTzgGXAb0XEBSS9AyuB9wGzImJhRJwH3Ffod5qNlrukzBL/ExE/KmC/JcD8nNleZ0iaEhFHc/bZAnSQBIUPpcsA04H7JP36KZRvCfCbwI703FNIpmt/NC3PnSRT3Xz7FL7brCAOGGaJnpzPg7x7+uvTcj4LuDhNwpRXROyRdCTNbPch4CPpps8Aj0ZEu6SzgW/lObyfd9/5D51bJPNf/d3wAySdT5JJ8BPAH5JkjzQbc+6SMhsmfeD9lqS5kipIuoKGbAdWDS1IumCEr9lMkgo3ExEvpOumcay76yMjHPczkm4m0in4h6Zk3w6s0LF89DMl/aqkepKH9FuBvyfJeWBWFA4YZvndTHIH8DjJlPhDVgGXSXpG0gvAjSMcvxX4E451R0GSQ/p2Sd8/wXm3Ao2SnibJMb0bkmn6gX8Atkt6hqTrqZEkoHw3nYl2PclMtGZF4dlqzcysIL7DMDOzgjhgmJlZQRwwzMysIA4YZmZWEAcMMzMriAOGmZkVxAHDzMwK8v92Fo4aM0q7wwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlr.plot_fitted()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This may take a little time. Have patience...\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 42 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlr.pairplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Outliers detection\n",
    "Outliers can also be an issue impacting the model quality by having a disproportionate influence on the estimated model parameters. We can use a measure called **Cook’s distance** to check for outliers. It essentially measures the effect of deleting a given observation. Points with a large Cook’s distance need to be closely examined for being potential outliers.\n",
    "\n",
    "We can create a special `Outliers` class for storing functions related to outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Outliers:\n",
    "    \n",
    "    def __init__():\n",
    "        pass\n",
    "    \n",
    "    def cook_distance(self):\n",
    "        '''Computes and plots Cook\\'s distance'''\n",
    "        import statsmodels.api as sm\n",
    "        from statsmodels.stats.outliers_influence import OLSInfluence as influence\n",
    "        lm = sm.OLS(self.target_, sm.add_constant(self.features_)).fit()\n",
    "        inf=influence(lm)\n",
    "        (c, p) = inf.cooks_distance\n",
    "        plt.figure(figsize=(8,5))\n",
    "        plt.title(\"Cook's distance plot for the residuals\",fontsize=14)\n",
    "        plt.stem(np.arange(len(c)), c, markerfmt=\",\", use_line_collection=True)\n",
    "        plt.grid(True)\n",
    "        plt.show()\n",
    "    \n",
    "    def influence_plot(self):\n",
    "        '''Creates the influence plot'''\n",
    "        import statsmodels.api as sm\n",
    "        lm = sm.OLS(self.target_, sm.add_constant(self.features_)).fit()\n",
    "        fig, ax = plt.subplots(figsize=(10,8))\n",
    "        fig = sm.graphics.influence_plot(lm, ax= ax, criterion=\"cooks\")\n",
    "        plt.show()\n",
    "    \n",
    "    def leverage_resid_plot(self):\n",
    "        '''Plots leverage vs normalized residuals' square'''\n",
    "        import statsmodels.api as sm\n",
    "        lm = sm.OLS(self.target_, sm.add_constant(self.features_)).fit()\n",
    "        fig, ax = plt.subplots(figsize=(10,8))\n",
    "        fig = sm.graphics.plot_leverage_resid2(lm, ax= ax)\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyLinearRegression(Metrics, Diagnostics_plots,Data_plots,Outliers):\n",
    "    \n",
    "    def __init__(self, fit_intercept=True):\n",
    "        self.coef_ = None\n",
    "        self.intercept_ = None\n",
    "        self._fit_intercept = fit_intercept\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return \"I am a Linear Regression model!\"\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        Fit model coefficients.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array \n",
    "        y: 1D numpy array\n",
    "        \"\"\"\n",
    "        \n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "        \n",
    "        # features and data\n",
    "        self.features_ = X\n",
    "        self.target_ = y\n",
    "        \n",
    "        # degrees of freedom of population dependent variable variance\n",
    "        self.dft_ = X.shape[0] - 1   \n",
    "        # degrees of freedom of population error variance\n",
    "        self.dfe_ = X.shape[0] - X.shape[1] - 1\n",
    "            \n",
    "        # add bias if fit_intercept is True\n",
    "        if self._fit_intercept:\n",
    "            X_biased = np.c_[np.ones(X.shape[0]), X]\n",
    "        else:\n",
    "            X_biased = X\n",
    "        \n",
    "        # closed form solution\n",
    "        xTx = np.dot(X_biased.T, X_biased)\n",
    "        inverse_xTx = np.linalg.inv(xTx)\n",
    "        xTy = np.dot(X_biased.T, y)\n",
    "        coef = np.dot(inverse_xTx, xTy)\n",
    "        \n",
    "        # set attributes\n",
    "        if self._fit_intercept:\n",
    "            self.intercept_ = coef[0]\n",
    "            self.coef_ = coef[1:]\n",
    "        else:\n",
    "            self.intercept_ = 0\n",
    "            self.coef_ = coef\n",
    "            \n",
    "        # Predicted/fitted y\n",
    "        self.fitted_ = np.dot(X,mlr.coef_) + mlr.intercept_\n",
    "        \n",
    "        # Residuals\n",
    "        residuals = self.target_ - self.fitted_\n",
    "        self.resid_ = residuals\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"Output model prediction.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array\n",
    "        \"\"\"\n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "        self.predicted_ = self.intercept_ + np.dot(X, self.coef_)\n",
    "        return self.predicted_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_samples=200\n",
    "num_dim = 5\n",
    "X = 10*np.random.random(size=(num_samples,num_dim))\n",
    "coeff = np.array([2,-3.5,1.2,4.1,-2.5])\n",
    "y = np.dot(coeff,X.T)+2*np.random.randn(num_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlr = MyLinearRegression()\n",
    "mlr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlr.cook_distance()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlr.influence_plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlr.leverage_resid_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Multicollinearity check\n",
    "For multiple linear regression, judging multicollinearity is also critical from the statistical inference point of view. This assumption assumes minimal or no linear dependence between the predicting variables.\n",
    "\n",
    "We can compute the **variance influence factors (VIF)** for each predicting variable. It is the ratio of variance in a model with multiple terms, divided by the variance of a model with one term alone.\n",
    "\n",
    "We create another special class `Multicollinearity` for this purpose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Multicollinearity:\n",
    "    \n",
    "    def __init__():\n",
    "        pass\n",
    "    \n",
    "    def vif(self):\n",
    "        '''Computes variance influence factors for each feature variable'''\n",
    "        import statsmodels.api as sm\n",
    "        from statsmodels.stats.outliers_influence import variance_inflation_factor as vif\n",
    "        lm = sm.OLS(self.target_, sm.add_constant(self.features_)).fit()\n",
    "        for i in range(self.features_.shape[1]):\n",
    "            v=vif(np.matrix(self.features_),i)\n",
    "            print(\"Variance inflation factor for feature {}: {}\".format(i,round(v,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyLinearRegression(Metrics, Diagnostics_plots,Data_plots,Outliers,Multicollinearity):\n",
    "    \n",
    "    def __init__(self, fit_intercept=True):\n",
    "        self.coef_ = None\n",
    "        self.intercept_ = None\n",
    "        self._fit_intercept = fit_intercept\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return \"I am a Linear Regression model!\"\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        Fit model coefficients.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array \n",
    "        y: 1D numpy array\n",
    "        \"\"\"\n",
    "        \n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "        \n",
    "        # features and data\n",
    "        self.features_ = X\n",
    "        self.target_ = y\n",
    "        \n",
    "        # degrees of freedom of population dependent variable variance\n",
    "        self.dft_ = X.shape[0] - 1   \n",
    "        # degrees of freedom of population error variance\n",
    "        self.dfe_ = X.shape[0] - X.shape[1] - 1\n",
    "            \n",
    "        # add bias if fit_intercept is True\n",
    "        if self._fit_intercept:\n",
    "            X_biased = np.c_[np.ones(X.shape[0]), X]\n",
    "        else:\n",
    "            X_biased = X\n",
    "        \n",
    "        # closed form solution\n",
    "        xTx = np.dot(X_biased.T, X_biased)\n",
    "        inverse_xTx = np.linalg.inv(xTx)\n",
    "        xTy = np.dot(X_biased.T, y)\n",
    "        coef = np.dot(inverse_xTx, xTy)\n",
    "        \n",
    "        # set attributes\n",
    "        if self._fit_intercept:\n",
    "            self.intercept_ = coef[0]\n",
    "            self.coef_ = coef[1:]\n",
    "        else:\n",
    "            self.intercept_ = 0\n",
    "            self.coef_ = coef\n",
    "            \n",
    "        # Predicted/fitted y\n",
    "        self.fitted_ = np.dot(X,mlr.coef_) + mlr.intercept_\n",
    "        \n",
    "        # Residuals\n",
    "        residuals = self.target_ - self.fitted_\n",
    "        self.resid_ = residuals\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"Output model prediction.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array\n",
    "        \"\"\"\n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "        self.predicted_ = self.intercept_ + np.dot(X, self.coef_)\n",
    "        return self.predicted_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_samples=200\n",
    "num_dim = 5\n",
    "X = 10*np.random.random(size=(num_samples,num_dim))\n",
    "coeff = np.array([2,-3.5,1.2,4.1,-2.5])\n",
    "y = np.dot(coeff,X.T)+2*np.random.randn(num_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlr = MyLinearRegression()\n",
    "mlr.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variance inflation factor for feature 0: 2.97\n",
      "Variance inflation factor for feature 1: 3.58\n",
      "Variance inflation factor for feature 2: 3.94\n",
      "Variance inflation factor for feature 3: 3.84\n",
      "Variance inflation factor for feature 4: 3.33\n"
     ]
    }
   ],
   "source": [
    "mlr.vif()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Syntactic sugar - `run_diagnostics` and `outlier_plots` methods added to the main class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyLinearRegression(Metrics, Diagnostics_plots,Data_plots,Outliers,Multicollinearity):\n",
    "    \n",
    "    def __init__(self, fit_intercept=True):\n",
    "        self.coef_ = None\n",
    "        self.intercept_ = None\n",
    "        self._fit_intercept = fit_intercept\n",
    "    \n",
    "    def __repr__(self):\n",
    "        return \"I am a Linear Regression model!\"\n",
    "    \n",
    "    def fit(self, X, y):\n",
    "        \"\"\"\n",
    "        Fit model coefficients.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array \n",
    "        y: 1D numpy array\n",
    "        \"\"\"\n",
    "        \n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "        \n",
    "        # features and data\n",
    "        self.features_ = X\n",
    "        self.target_ = y\n",
    "        \n",
    "        # degrees of freedom of population dependent variable variance\n",
    "        self.dft_ = X.shape[0] - 1   \n",
    "        # degrees of freedom of population error variance\n",
    "        self.dfe_ = X.shape[0] - X.shape[1] - 1\n",
    "            \n",
    "        # add bias if fit_intercept is True\n",
    "        if self._fit_intercept:\n",
    "            X_biased = np.c_[np.ones(X.shape[0]), X]\n",
    "        else:\n",
    "            X_biased = X\n",
    "        \n",
    "        # closed form solution\n",
    "        xTx = np.dot(X_biased.T, X_biased)\n",
    "        inverse_xTx = np.linalg.inv(xTx)\n",
    "        xTy = np.dot(X_biased.T, y)\n",
    "        coef = np.dot(inverse_xTx, xTy)\n",
    "        \n",
    "        # set attributes\n",
    "        if self._fit_intercept:\n",
    "            self.intercept_ = coef[0]\n",
    "            self.coef_ = coef[1:]\n",
    "        else:\n",
    "            self.intercept_ = 0\n",
    "            self.coef_ = coef\n",
    "            \n",
    "        # Predicted/fitted y\n",
    "        self.fitted_ = np.dot(X,mlr.coef_) + mlr.intercept_\n",
    "        \n",
    "        # Residuals\n",
    "        residuals = self.target_ - self.fitted_\n",
    "        self.resid_ = residuals\n",
    "    \n",
    "    def predict(self, X):\n",
    "        \"\"\"Output model prediction.\n",
    "\n",
    "        Arguments:\n",
    "        X: 1D or 2D numpy array\n",
    "        \"\"\"\n",
    "        # check if X is 1D or 2D array\n",
    "        if len(X.shape) == 1:\n",
    "            X = X.reshape(-1,1)\n",
    "        self.predicted_ = self.intercept_ + np.dot(X, self.coef_)\n",
    "        return self.predicted_\n",
    "    \n",
    "    def run_diagnostics(self):\n",
    "        '''Runs diagnostics tests and plots'''\n",
    "        Diagnostics_plots.fitted_vs_residual(self)\n",
    "        Diagnostics_plots.histogram_resid(self)\n",
    "        Diagnostics_plots.qqplot_resid(self)\n",
    "        print()\n",
    "        Diagnostics_plots.shapiro_test(self)\n",
    "    \n",
    "    def outlier_plots(self):\n",
    "        '''Creates various outlier plots'''\n",
    "        Outliers.cook_distance(self)\n",
    "        Outliers.influence_plot(self)\n",
    "        Outliers.leverage_resid_plot(self)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can generate data and test these new methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_samples=200\n",
    "num_dim = 5\n",
    "X = 10*np.random.random(size=(num_samples,num_dim))\n",
    "coeff = np.array([2,-3.5,1.2,4.1,-2.5])\n",
    "y = np.dot(coeff,X.T)+2*np.random.randn(num_samples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlr = MyLinearRegression()\n",
    "mlr.fit(X,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Running all diagnostics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The residuals seem to have come from a Gaussian process\n"
     ]
    }
   ],
   "source": [
    "mlr.run_diagnostics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### All outlier plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mlr.outlier_plots()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  },
  "toc-showcode": false,
  "toc-showmarkdowntxt": false
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
